AI/ML Engineer

General Information Job Title AI/ML Engineer Job ID 108806 Work Areas Technology & Engineering Employment Type Permanent Full-Time Location(s) Atlanta, Austin, Chicago, Dallas, Houston Description & Requirements WHAT MAKES US A GREAT PLACE TO WORKWe are proud to be consistently recognized as one of the world’s best places to work. We are currently the top ranked consulting firm on Glassdoor’s Best Places to Work list and have earned the #1 overall spot a record seven times.Extraordinary teams are at the heart of our business strategy, but these don’t happen by chance. They require intentional focus on bringing together a broad set of backgrounds, cultures, experiences, perspectives, and skills in a supportive and inclusive work environment. We hire people with exceptional talent and create an environment in which every individual can thrive professionally and personally.WHO YOU’LL WORK WITHAs the premier consulting partner for the private equity industry, Bain's PEG boasts a global practice that is over three times larger than any competitor. Our network of over 1,000 professionals supports private equity and institutional investor clients through every stage of the investment life cycle, from deal generation and due diligence to portfolio value creation and exit planning.Bain & Company is developing a suite of cutting-edge data and software solutions designed to revolutionize how the private equity industry uses data for investment insights and decision-making. The PEG Innovation team's mission is to create analytical solutions for Bain clients, teams, and the broader institutional investor space using proprietary software and data products. This includes the development, commercialization, and daily management of Bain's proprietary datasets, data, and software businesses.WHERE YOU’LL FIT WITHIN THE TEAMAI/ML Engineers on the Diligence Platform build and maintain the data, feature, and retrieval pipelines that power production RAG and ML systems. You work under the guidance of Senior ML Engineers and the Engineering Manager to implement and operate components of the ingestion, embedding, and retrieval stack, ship well-tested production code, and grow your ownership of these systems over time. You partner with Data Engineers, Data Scientists, and the Agent / AI squad on defined pieces of larger pipelines and RAG workstreams, and you build the habits, tooling fluency, and production judgment expected of a Senior ML Engineer. This is a hands-on, growth-oriented engineering role: you are expected to ship reliable, observable code from your first weeks, and to take on increasing ownership as your track record builds.WHAT YOU'LL DOCore ML and Data Pipeline Engineering (65%) Implement and maintain components of production data and ML pipelines: ingestion jobs, feature and embedding pipelines, and Celery-based workers, under the direction of senior engineers.Build and support pieces of the RAG and retrieval stack: chunking, embedding calls, indexing into pgvector, and basic retrieval and re-ranking logic, following established patterns.Write production-quality Python: type hints, tests, and linting to the team's standards, with code reviewed by senior engineers before merge.Instrument the pipelines and services you own with structured logs and metrics, and help build the dashboards and alerts that make issues visible.Reproduce, triage, and fix bugs in pipeline and serving code, escalating ambiguous or high-severity issues to senior engineers.Collaboration and Support (25%)Partner with Data Engineers, Data Scientists, and the Agent / AI squad on defined tasks within larger pipeline, retrieval, and evaluation workstreams.Contribute test cases and sample data to evaluation harnesses and golden datasets, under the direction of senior engineers.Participate in design reviews and code reviews, both as reviewer and reviewee, building judgment about production trade-offs.Keep runbooks, READMEs, and pipeline documentation current as you build and change the systems you touch.Other (10%):Use AI coding assistants to accelerate scaffolding and boilerplate, and review generated code against team standards before committing.Use LLMs to draft documentation and status notes; validate and refine outputs before sharing them.Take on interviewing and hiring-loop participation as your experience grows.ABOUT YOUBachelor's degree in Computer Science, Engineering, Machine Learning, Data Science, Statistics, or a related field (or equivalent practical experience).2+ years of experience building software, data, or ML systems, ideally including some exposure to production pipelines or services.Exposure to model deployment, serving, or monitoring is a plus.Experience working with structured feedback and code review, and a track record of improving code quality over time.Experience collaborating with Data Engineers, Data Scientists, or the Agent / AI squad to ship features that depend on retrieval or ML outputs.Comfort with Python as a primary language; exposure to a modern cloud environment (Databricks, Azure, or AWS) is a plus.Demonstrated ability to take a well-scoped task from specification to a tested, reviewed implementation with limited supervision.ML engineering / LLMOpsWorking knowledge of Python for data and ML workloads: type hints, Pydantic, pytest, Ruff, with production-quality pipeline and serving code that would pass a code review.Familiarity with MLflow concepts: experiment tracking, model registry, and promotion workflows.Exposure to LLMOps concepts: prompt versioning, model gateways (e.g., Portkey), and inference orchestration frameworks (LangChain, LlamaIndex, or equivalent).Good understanding of model-serving concepts: latency, throughput, and batching, even without direct production ownership yet.RAG pipeline building blocks: chunking strategies, embeddings, and vector stores such as pgvector; able to contribute to indexing and retrieval jobs under senior guidance.Understanding of model and pipeline evaluation basics: what a golden dataset is, and why regression gates matter in CI.Docker: comfortable containerising pipeline or serving code and running it locally for testing.Git: confident with PR-based workflows.Generative AI and agentic systemsContributes to inference and retrieval services that feed agent workflows as structured tool responses.Supports RAG quality work: helps build and run recall and precision checks against defined benchmarks.Exposure to LLM-as-judge evaluation patterns, even if applied under senior-engineer direction.GeneralTreats testing, observability, and documentation as part of the job, not an afterthought, even on smaller tasks.Raises questions and surfaces uncertainty early rather than guessing silently on ambiguous requirements.Uses AI tooling to move faster, and reviews all generated code and documentation critically before it enters the codebase.Communicates clearly with teammates about progress, blockers, and trade-offs; asks for help early.This role follows a hybrid model, requiring in-office presence at least 1 day per weekU.S. COMPENSATION INFORMATIONCompensation for this role includes base salary, annual discretionary performance bonus, 401(k) plan with an annual employer contribution based on years of service and Bain’s best in class benefits package (details listed below).Some local governments in the United States require a good-faith, reasonable salary range be included in job postings for open roles. The estimated annualized compensation for this role is as follows:In Atlanta, the good-faith, reasonable annualized full-time salary range for this role is between $72,000 - $86,500In Texas, the good-faith, reasonable annualized full-time salary range for this role is between $75,750 - $90,750In Chicago, the good-faith, reasonable annualized full-time salary range for this role is between $79,500 - $95,250Placement within these ranges will vary based on factors such as experience, education, training, and skill level.Compensation also includes a discretionary annual performance bonus, 401(k) plan with employer contribution, and Bain’s best-in-class benefits—including full premium coverage for medical, dental, and vision, generous paid time off, and more.Annual discretionary performance bonus This role may also be eligible for other elements of discretionary compensation4.5% 401(k) company contribution, which increases after 3 years of service and is 100% vested upon start dateBain & Company's comprehensive benefits and wellness program is designed to help employees achieve personal independence, protection and stability in the areas most important to you and your family.Bain pays 100% individual employee premiums for medical, dental and vision programs, offering one of the most comprehensive medical plans for employees without impacting your paycheckGenerous paid time off, including parental leave, sick leave and paid holidaysFully vested 401(k) company contributionPaid Life and Long-Term Disability insurance Go backGeneral Information Job Title AI/ML Engineer Job ID 108806 Work Areas Technology & Engineering Employment Type Permanent Full-Time Location(s) Atlanta, Austin, Chicago, Dallas, Houston Description & Requirements WHAT MAKES US A GREAT PLACE TO WORKWe are proud to be consistently recognized as one of the world’s best places to work. We are currently the top ranked consulting firm on Glassdoor’s Best Places to Work list and have earned the #1 overall spot a record seven times.Extraordinary teams are at the heart of our business strategy, but these don’t happen by chance. They require intentional focus on bringing together a broad set of backgrounds, cultures, experiences, perspectives, and skills in a supportive and inclusive work environment. We hire people with exceptional talent and create an environment in which every individual can thrive professionally and personally.WHO YOU’LL WORK WITHAs the premier consulting partner for the private equity industry, Bain's PEG boasts a global practice that is over three times larger than any competitor. Our network of over 1,000 professionals supports private equity and institutional investor clients through every stage of the investment life cycle, from deal generation and due diligence to portfolio value creation and exit planning.Bain & Company is developing a suite of cutting-edge data and software solutions designed to revolutionize how the private equity industry uses data for investment insights and decision-making. The PEG Innovation team's mission is to create analytical solutions for Bain clients, teams, and the broader institutional investor space using proprietary software and data products. This includes the development, commercialization, and daily management of Bain's proprietary datasets, data, and software businesses.WHERE YOU’LL FIT WITHIN THE TEAMAI/ML Engineers on the Diligence Platform build and maintain the data, feature, and retrieval pipelines that power production RAG and ML systems. You work under the guidance of Senior ML Engineers and the Engineering Manager to implement and operate components of the ingestion, embedding, and retrieval stack, ship well-tested production code, and grow your ownership of these systems over time. You partner with Data Engineers, Data Scientists, and the Agent / AI squad on defined pieces of larger pipelines and RAG workstreams, and you build the habits, tooling fluency, and production judgment expected of a Senior ML Engineer. This is a hands-on, growth-oriented engineering role: you are expected to ship reliable, observable code from your first weeks, and to take on increasing ownership as your track record builds.WHAT YOU'LL DOCore ML and Data Pipeline Engineering (65%) Implement and maintain components of production data and ML pipelines: ingestion jobs, feature and embedding pipelines, and Celery-based workers, under the direction of senior engineers.Build and support pieces of the RAG and retrieval stack: chunking, embedding calls, indexing into pgvector, and basic retrieval and re-ranking logic, following established patterns.Write production-quality Python: type hints, tests, and linting to the team's standards, with code reviewed by senior engineers before merge.Instrument the pipelines and services you own with structured logs and metrics, and help build the dashboards and alerts that make issues visible.Reproduce, triage, and fix bugs in pipeline and serving code, escalating ambiguous or high-severity issues to senior engineers.Collaboration and Support (25%)Partner with Data Engineers, Data Scientists, and the Agent / AI squad on defined tasks within larger pipeline, retrieval, and evaluation workstreams.Contribute test cases and sample data to evaluation harnesses and golden datasets, under the direction of senior engineers.Participate in design reviews and code reviews, both as reviewer and reviewee, building judgment about production trade-offs.Keep runbooks, READMEs, and pipeline documentation current as you build and change the systems you touch.Other (10%):Use AI coding assistants to accelerate scaffolding and boilerplate, and review generated code against team standards before committing.Use LLMs to draft documentation and status notes; validate and refine outputs before sharing them.Take on interviewing and hiring-loop participation as your experience grows.ABOUT YOUBachelor's degree in Computer Science, Engineering, Machine Learning, Data Science, Statistics, or a related field (or equivalent practical experience).2+ years of experience building software, data, or ML systems, ideally including some exposure to production pipelines or services.Exposure to model deployment, serving, or monitoring is a plus.Experience working with structured feedback and code review, and a track record of improving code quality over time.Experience collaborating with Data Engineers, Data Scientists, or the Agent / AI squad to ship features that depend on retrieval or ML outputs.Comfort with Python as a primary language; exposure to a modern cloud environment (Databricks, Azure, or AWS) is a plus.Demonstrated ability to take a well-scoped task from specification to a tested, reviewed implementation with limited supervision.ML engineering / LLMOpsWorking knowledge of Python for data and ML workloads: type hints, Pydantic, pytest, Ruff, with production-quality pipeline and serving code that would pass a code review.Familiarity with MLflow concepts: experiment tracking, model registry, and promotion workflows.Exposure to LLMOps concepts: prompt versioning, model gateways (e.g., Portkey), and inference orchestration frameworks (LangChain, LlamaIndex, or equivalent).Good understanding of model-serving concepts: latency, throughput, and batching, even without direct production ownership yet.RAG pipeline building blocks: chunking strategies, embeddings, and vector stores such as pgvector; able to contribute to indexing and retrieval jobs under senior guidance.Understanding of model and pipeline evaluation basics: what a golden dataset is, and why regression gates matter in CI.Docker: comfortable containerising pipeline or serving code and running it locally for testing.Git: confident with PR-based workflows.Generative AI and agentic systemsContributes to inference and retrieval services that feed agent workflows as structured tool responses.Supports RAG quality work: helps build and run recall and precision checks against defined benchmarks.Exposure to LLM-as-judge evaluation patterns, even if applied under senior-engineer direction.GeneralTreats testing, observability, and documentation as part of the job, not an afterthought, even on smaller tasks.Raises questions and surfaces uncertainty early rather than guessing silently on ambiguous requirements.Uses AI tooling to move faster, and reviews all generated code and documentation critically before it enters the codebase.Communicates clearly with teammates about progress, blockers, and trade-offs; asks for help early.This role follows a hybrid model, requiring in-office presence at least 1 day per weekU.S. COMPENSATION INFORMATIONCompensation for this role includes base salary, annual discretionary performance bonus, 401(k) plan with an annual employer contribution based on years of service and Bain’s best in class benefits package (details listed below).Some local governments in the United States require a good-faith, reasonable salary range be included in job postings for open roles. The estimated annualized compensation for this role is as follows:In Atlanta, the good-faith, reasonable annualized full-time salary range for this role is between $72,000 - $86,500In Texas, the good-faith, reasonable annualized full-time salary range for this role is between $75,750 - $90,750In Chicago, the good-faith, reasonable annualized full-time salary range for this role is between $79,500 - $95,250Placement within these ranges will vary based on factors such as experience, education, training, and skill level.Compensation also includes a discretionary annual performance bonus, 401(k) plan with employer contribution, and Bain’s best-in-class benefits—including full premium coverage for medical, dental, and vision, generous paid time off, and more.Annual discretionary performance bonus This role may also be eligible for other elements of discretionary compensation4.5% 401(k) company contribution, which increases after 3 years of service and is 100% vested upon start dateBain & Company's comprehensive benefits and wellness program is designed to help employees achieve personal independence, protection and stability in the areas most important to you and your family.Bain pays 100% individual employee premiums for medical, dental and vision programs, offering one of the most comprehensive medical plans for employees without impacting your paycheckGenerous paid time off, including parental leave, sick leave and paid holidaysFully vested 401(k) company contributionPaid Life and Long-Term Disability insuranceGeneral InformationGeneral Information

General Information

Job Title AI/ML Engineer Job ID 108806 Work Areas Technology & Engineering Employment Type Permanent Full-Time Location(s) Atlanta, Austin, Chicago, Dallas, HoustonJob Title AI/ML Engineer Job ID 108806 Work Areas Technology & Engineering Employment Type Permanent Full-Time Location(s) Atlanta, Austin, Chicago, Dallas, HoustonJob Title AI/ML EngineerJob TitleAI/ML EngineerJob ID 108806Job ID108806Work Areas Technology & EngineeringWork AreasTechnology & EngineeringEmployment Type Permanent Full-TimeEmployment TypePermanent Full-TimeLocation(s) Atlanta, Austin, Chicago, Dallas, HoustonLocation(s)Atlanta, Austin, Chicago, Dallas, HoustonDescription & RequirementsDescription & Requirements

Description & Requirements

WHAT MAKES US A GREAT PLACE TO WORKWe are proud to be consistently recognized as one of the world’s best places to work. We are currently the top ranked consulting firm on Glassdoor’s Best Places to Work list and have earned the #1 overall spot a record seven times.Extraordinary teams are at the heart of our business strategy, but these don’t happen by chance. They require intentional focus on bringing together a broad set of backgrounds, cultures, experiences, perspectives, and skills in a supportive and inclusive work environment. We hire people with exceptional talent and create an environment in which every individual can thrive professionally and personally.WHO YOU’LL WORK WITHAs the premier consulting partner for the private equity industry, Bain's PEG boasts a global practice that is over three times larger than any competitor. Our network of over 1,000 professionals supports private equity and institutional investor clients through every stage of the investment life cycle, from deal generation and due diligence to portfolio value creation and exit planning.Bain & Company is developing a suite of cutting-edge data and software solutions designed to revolutionize how the private equity industry uses data for investment insights and decision-making. The PEG Innovation team's mission is to create analytical solutions for Bain clients, teams, and the broader institutional investor space using proprietary software and data products. This includes the development, commercialization, and daily management of Bain's proprietary datasets, data, and software businesses.WHERE YOU’LL FIT WITHIN THE TEAMAI/ML Engineers on the Diligence Platform build and maintain the data, feature, and retrieval pipelines that power production RAG and ML systems. You work under the guidance of Senior ML Engineers and the Engineering Manager to implement and operate components of the ingestion, embedding, and retrieval stack, ship well-tested production code, and grow your ownership of these systems over time. You partner with Data Engineers, Data Scientists, and the Agent / AI squad on defined pieces of larger pipelines and RAG workstreams, and you build the habits, tooling fluency, and production judgment expected of a Senior ML Engineer. This is a hands-on, growth-oriented engineering role: you are expected to ship reliable, observable code from your first weeks, and to take on increasing ownership as your track record builds.WHAT YOU'LL DOCore ML and Data Pipeline Engineering (65%) Implement and maintain components of production data and ML pipelines: ingestion jobs, feature and embedding pipelines, and Celery-based workers, under the direction of senior engineers.Build and support pieces of the RAG and retrieval stack: chunking, embedding calls, indexing into pgvector, and basic retrieval and re-ranking logic, following established patterns.Write production-quality Python: type hints, tests, and linting to the team's standards, with code reviewed by senior engineers before merge.Instrument the pipelines and services you own with structured logs and metrics, and help build the dashboards and alerts that make issues visible.Reproduce, triage, and fix bugs in pipeline and serving code, escalating ambiguous or high-severity issues to senior engineers.Collaboration and Support (25%)Partner with Data Engineers, Data Scientists, and the Agent / AI squad on defined tasks within larger pipeline, retrieval, and evaluation workstreams.Contribute test cases and sample data to evaluation harnesses and golden datasets, under the direction of senior engineers.Participate in design reviews and code reviews, both as reviewer and reviewee, building judgment about production trade-offs.Keep runbooks, READMEs, and pipeline documentation current as you build and change the systems you touch.Other (10%):Use AI coding assistants to accelerate scaffolding and boilerplate, and review generated code against team standards before committing.Use LLMs to draft documentation and status notes; validate and refine outputs before sharing them.Take on interviewing and hiring-loop participation as your experience grows.ABOUT YOUBachelor's degree in Computer Science, Engineering, Machine Learning, Data Science, Statistics, or a related field (or equivalent practical experience).2+ years of experience building software, data, or ML systems, ideally including some exposure to production pipelines or services.Exposure to model deployment, serving, or monitoring is a plus.Experience working with structured feedback and code review, and a track record of improving code quality over time.Experience collaborating with Data Engineers, Data Scientists, or the Agent / AI squad to ship features that depend on retrieval or ML outputs.Comfort with Python as a primary language; exposure to a modern cloud environment (Databricks, Azure, or AWS) is a plus.Demonstrated ability to take a well-scoped task from specification to a tested, reviewed implementation with limited supervision.ML engineering / LLMOpsWorking knowledge of Python for data and ML workloads: type hints, Pydantic, pytest, Ruff, with production-quality pipeline and serving code that would pass a code review.Familiarity with MLflow concepts: experiment tracking, model registry, and promotion workflows.Exposure to LLMOps concepts: prompt versioning, model gateways (e.g., Portkey), and inference orchestration frameworks (LangChain, LlamaIndex, or equivalent).Good understanding of model-serving concepts: latency, throughput, and batching, even without direct production ownership yet.RAG pipeline building blocks: chunking strategies, embeddings, and vector stores such as pgvector; able to contribute to indexing and retrieval jobs under senior guidance.Understanding of model and pipeline evaluation basics: what a golden dataset is, and why regression gates matter in CI.Docker: comfortable containerising pipeline or serving code and running it locally for testing.Git: confident with PR-based workflows.Generative AI and agentic systemsContributes to inference and retrieval services that feed agent workflows as structured tool responses.Supports RAG quality work: helps build and run recall and precision checks against defined benchmarks.Exposure to LLM-as-judge evaluation patterns, even if applied under senior-engineer direction.GeneralTreats testing, observability, and documentation as part of the job, not an afterthought, even on smaller tasks.Raises questions and surfaces uncertainty early rather than guessing silently on ambiguous requirements.Uses AI tooling to move faster, and reviews all generated code and documentation critically before it enters the codebase.Communicates clearly with teammates about progress, blockers, and trade-offs; asks for help early.This role follows a hybrid model, requiring in-office presence at least 1 day per weekU.S. COMPENSATION INFORMATIONCompensation for this role includes base salary, annual discretionary performance bonus, 401(k) plan with an annual employer contribution based on years of service and Bain’s best in class benefits package (details listed below).Some local governments in the United States require a good-faith, reasonable salary range be included in job postings for open roles. The estimated annualized compensation for this role is as follows:In Atlanta, the good-faith, reasonable annualized full-time salary range for this role is between $72,000 - $86,500In Texas, the good-faith, reasonable annualized full-time salary range for this role is between $75,750 - $90,750In Chicago, the good-faith, reasonable annualized full-time salary range for this role is between $79,500 - $95,250Placement within these ranges will vary based on factors such as experience, education, training, and skill level.Compensation also includes a discretionary annual performance bonus, 401(k) plan with employer contribution, and Bain’s best-in-class benefits—including full premium coverage for medical, dental, and vision, generous paid time off, and more.Annual discretionary performance bonus This role may also be eligible for other elements of discretionary compensation4.5% 401(k) company contribution, which increases after 3 years of service and is 100% vested upon start dateBain & Company's comprehensive benefits and wellness program is designed to help employees achieve personal independence, protection and stability in the areas most important to you and your family.Bain pays 100% individual employee premiums for medical, dental and vision programs, offering one of the most comprehensive medical plans for employees without impacting your paycheckGenerous paid time off, including parental leave, sick leave and paid holidaysFully vested 401(k) company contributionPaid Life and Long-Term Disability insuranceWHAT MAKES US A GREAT PLACE TO WORKWe are proud to be consistently recognized as one of the world’s best places to work. We are currently the top ranked consulting firm on Glassdoor’s Best Places to Work list and have earned the #1 overall spot a record seven times.Extraordinary teams are at the heart of our business strategy, but these don’t happen by chance. They require intentional focus on bringing together a broad set of backgrounds, cultures, experiences, perspectives, and skills in a supportive and inclusive work environment. We hire people with exceptional talent and create an environment in which every individual can thrive professionally and personally.WHO YOU’LL WORK WITHAs the premier consulting partner for the private equity industry, Bain's PEG boasts a global practice that is over three times larger than any competitor. Our network of over 1,000 professionals supports private equity and institutional investor clients through every stage of the investment life cycle, from deal generation and due diligence to portfolio value creation and exit planning.Bain & Company is developing a suite of cutting-edge data and software solutions designed to revolutionize how the private equity industry uses data for investment insights and decision-making. The PEG Innovation team's mission is to create analytical solutions for Bain clients, teams, and the broader institutional investor space using proprietary software and data products. This includes the development, commercialization, and daily management of Bain's proprietary datasets, data, and software businesses.WHERE YOU’LL FIT WITHIN THE TEAMAI/ML Engineers on the Diligence Platform build and maintain the data, feature, and retrieval pipelines that power production RAG and ML systems. You work under the guidance of Senior ML Engineers and the Engineering Manager to implement and operate components of the ingestion, embedding, and retrieval stack, ship well-tested production code, and grow your ownership of these systems over time. You partner with Data Engineers, Data Scientists, and the Agent / AI squad on defined pieces of larger pipelines and RAG workstreams, and you build the habits, tooling fluency, and production judgment expected of a Senior ML Engineer. This is a hands-on, growth-oriented engineering role: you are expected to ship reliable, observable code from your first weeks, and to take on increasing ownership as your track record builds.WHAT YOU'LL DOCore ML and Data Pipeline Engineering (65%) Implement and maintain components of production data and ML pipelines: ingestion jobs, feature and embedding pipelines, and Celery-based workers, under the direction of senior engineers.Build and support pieces of the RAG and retrieval stack: chunking, embedding calls, indexing into pgvector, and basic retrieval and re-ranking logic, following established patterns.Write production-quality Python: type hints, tests, and linting to the team's standards, with code reviewed by senior engineers before merge.Instrument the pipelines and services you own with structured logs and metrics, and help build the dashboards and alerts that make issues visible.Reproduce, triage, and fix bugs in pipeline and serving code, escalating ambiguous or high-severity issues to senior engineers.Collaboration and Support (25%)Partner with Data Engineers, Data Scientists, and the Agent / AI squad on defined tasks within larger pipeline, retrieval, and evaluation workstreams.Contribute test cases and sample data to evaluation harnesses and golden datasets, under the direction of senior engineers.Participate in design reviews and code reviews, both as reviewer and reviewee, building judgment about production trade-offs.Keep runbooks, READMEs, and pipeline documentation current as you build and change the systems you touch.Other (10%):Use AI coding assistants to accelerate scaffolding and boilerplate, and review generated code against team standards before committing.Use LLMs to draft documentation and status notes; validate and refine outputs before sharing them.Take on interviewing and hiring-loop participation as your experience grows.ABOUT YOUBachelor's degree in Computer Science, Engineering, Machine Learning, Data Science, Statistics, or a related field (or equivalent practical experience).2+ years of experience building software, data, or ML systems, ideally including some exposure to production pipelines or services.Exposure to model deployment, serving, or monitoring is a plus.Experience working with structured feedback and code review, and a track record of improving code quality over time.Experience collaborating with Data Engineers, Data Scientists, or the Agent / AI squad to ship features that depend on retrieval or ML outputs.Comfort with Python as a primary language; exposure to a modern cloud environment (Databricks, Azure, or AWS) is a plus.Demonstrated ability to take a well-scoped task from specification to a tested, reviewed implementation with limited supervision.ML engineering / LLMOpsWorking knowledge of Python for data and ML workloads: type hints, Pydantic, pytest, Ruff, with production-quality pipeline and serving code that would pass a code review.Familiarity with MLflow concepts: experiment tracking, model registry, and promotion workflows.Exposure to LLMOps concepts: prompt versioning, model gateways (e.g., Portkey), and inference orchestration frameworks (LangChain, LlamaIndex, or equivalent).Good understanding of model-serving concepts: latency, throughput, and batching, even without direct production ownership yet.RAG pipeline building blocks: chunking strategies, embeddings, and vector stores such as pgvector; able to contribute to indexing and retrieval jobs under senior guidance.Understanding of model and pipeline evaluation basics: what a golden dataset is, and why regression gates matter in CI.Docker: comfortable containerising pipeline or serving code and running it locally for testing.Git: confident with PR-based workflows.Generative AI and agentic systemsContributes to inference and retrieval services that feed agent workflows as structured tool responses.Supports RAG quality work: helps build and run recall and precision checks against defined benchmarks.Exposure to LLM-as-judge evaluation patterns, even if applied under senior-engineer direction.GeneralTreats testing, observability, and documentation as part of the job, not an afterthought, even on smaller tasks.Raises questions and surfaces uncertainty early rather than guessing silently on ambiguous requirements.Uses AI tooling to move faster, and reviews all generated code and documentation critically before it enters the codebase.Communicates clearly with teammates about progress, blockers, and trade-offs; asks for help early.This role follows a hybrid model, requiring in-office presence at least 1 day per weekU.S. COMPENSATION INFORMATIONCompensation for this role includes base salary, annual discretionary performance bonus, 401(k) plan with an annual employer contribution based on years of service and Bain’s best in class benefits package (details listed below).Some local governments in the United States require a good-faith, reasonable salary range be included in job postings for open roles. The estimated annualized compensation for this role is as follows:In Atlanta, the good-faith, reasonable annualized full-time salary range for this role is between $72,000 - $86,500In Texas, the good-faith, reasonable annualized full-time salary range for this role is between $75,750 - $90,750In Chicago, the good-faith, reasonable annualized full-time salary range for this role is between $79,500 - $95,250Placement within these ranges will vary based on factors such as experience, education, training, and skill level.Compensation also includes a discretionary annual performance bonus, 401(k) plan with employer contribution, and Bain’s best-in-class benefits—including full premium coverage for medical, dental, and vision, generous paid time off, and more.Annual discretionary performance bonus This role may also be eligible for other elements of discretionary compensation4.5% 401(k) company contribution, which increases after 3 years of service and is 100% vested upon start dateBain & Company's comprehensive benefits and wellness program is designed to help employees achieve personal independence, protection and stability in the areas most important to you and your family.Bain pays 100% individual employee premiums for medical, dental and vision programs, offering one of the most comprehensive medical plans for employees without impacting your paycheckGenerous paid time off, including parental leave, sick leave and paid holidaysFully vested 401(k) company contributionPaid Life and Long-Term Disability insuranceWHAT MAKES US A GREAT PLACE TO WORKWe are proud to be consistently recognized as one of the world’s best places to work. We are currently the top ranked consulting firm on Glassdoor’s Best Places to Work list and have earned the #1 overall spot a record seven times.Extraordinary teams are at the heart of our business strategy, but these don’t happen by chance. They require intentional focus on bringing together a broad set of backgrounds, cultures, experiences, perspectives, and skills in a supportive and inclusive work environment. We hire people with exceptional talent and create an environment in which every individual can thrive professionally and personally.WHO YOU’LL WORK WITHAs the premier consulting partner for the private equity industry, Bain's PEG boasts a global practice that is over three times larger than any competitor. Our network of over 1,000 professionals supports private equity and institutional investor clients through every stage of the investment life cycle, from deal generation and due diligence to portfolio value creation and exit planning.Bain & Company is developing a suite of cutting-edge data and software solutions designed to revolutionize how the private equity industry uses data for investment insights and decision-making. The PEG Innovation team's mission is to create analytical solutions for Bain clients, teams, and the broader institutional investor space using proprietary software and data products. This includes the development, commercialization, and daily management of Bain's proprietary datasets, data, and software businesses.WHERE YOU’LL FIT WITHIN THE TEAMAI/ML Engineers on the Diligence Platform build and maintain the data, feature, and retrieval pipelines that power production RAG and ML systems. You work under the guidance of Senior ML Engineers and the Engineering Manager to implement and operate components of the ingestion, embedding, and retrieval stack, ship well-tested production code, and grow your ownership of these systems over time. You partner with Data Engineers, Data Scientists, and the Agent / AI squad on defined pieces of larger pipelines and RAG workstreams, and you build the habits, tooling fluency, and production judgment expected of a Senior ML Engineer. This is a hands-on, growth-oriented engineering role: you are expected to ship reliable, observable code from your first weeks, and to take on increasing ownership as your track record builds.WHAT YOU'LL DOCore ML and Data Pipeline Engineering (65%) Implement and maintain components of production data and ML pipelines: ingestion jobs, feature and embedding pipelines, and Celery-based workers, under the direction of senior engineers.Build and support pieces of the RAG and retrieval stack: chunking, embedding calls, indexing into pgvector, and basic retrieval and re-ranking logic, following established patterns.Write production-quality Python: type hints, tests, and linting to the team's standards, with code reviewed by senior engineers before merge.Instrument the pipelines and services you own with structured logs and metrics, and help build the dashboards and alerts that make issues visible.Reproduce, triage, and fix bugs in pipeline and serving code, escalating ambiguous or high-severity issues to senior engineers.Collaboration and Support (25%)Partner with Data Engineers, Data Scientists, and the Agent / AI squad on defined tasks within larger pipeline, retrieval, and evaluation workstreams.Contribute test cases and sample data to evaluation harnesses and golden datasets, under the direction of senior engineers.Participate in design reviews and code reviews, both as reviewer and reviewee, building judgment about production trade-offs.Keep runbooks, READMEs, and pipeline documentation current as you build and change the systems you touch.Other (10%):Use AI coding assistants to accelerate scaffolding and boilerplate, and review generated code against team standards before committing.Use LLMs to draft documentation and status notes; validate and refine outputs before sharing them.Take on interviewing and hiring-loop participation as your experience grows.ABOUT YOUBachelor's degree in Computer Science, Engineering, Machine Learning, Data Science, Statistics, or a related field (or equivalent practical experience).2+ years of experience building software, data, or ML systems, ideally including some exposure to production pipelines or services.Exposure to model deployment, serving, or monitoring is a plus.Experience working with structured feedback and code review, and a track record of improving code quality over time.Experience collaborating with Data Engineers, Data Scientists, or the Agent / AI squad to ship features that depend on retrieval or ML outputs.Comfort with Python as a primary language; exposure to a modern cloud environment (Databricks, Azure, or AWS) is a plus.Demonstrated ability to take a well-scoped task from specification to a tested, reviewed implementation with limited supervision.ML engineering / LLMOpsWorking knowledge of Python for data and ML workloads: type hints, Pydantic, pytest, Ruff, with production-quality pipeline and serving code that would pass a code review.Familiarity with MLflow concepts: experiment tracking, model registry, and promotion workflows.Exposure to LLMOps concepts: prompt versioning, model gateways (e.g., Portkey), and inference orchestration frameworks (LangChain, LlamaIndex, or equivalent).Good understanding of model-serving concepts: latency, throughput, and batching, even without direct production ownership yet.RAG pipeline building blocks: chunking strategies, embeddings, and vector stores such as pgvector; able to contribute to indexing and retrieval jobs under senior guidance.Understanding of model and pipeline evaluation basics: what a golden dataset is, and why regression gates matter in CI.Docker: comfortable containerising pipeline or serving code and running it locally for testing.Git: confident with PR-based workflows.Generative AI and agentic systemsContributes to inference and retrieval services that feed agent workflows as structured tool responses.Supports RAG quality work: helps build and run recall and precision checks against defined benchmarks.Exposure to LLM-as-judge evaluation patterns, even if applied under senior-engineer direction.GeneralTreats testing, observability, and documentation as part of the job, not an afterthought, even on smaller tasks.Raises questions and surfaces uncertainty early rather than guessing silently on ambiguous requirements.Uses AI tooling to move faster, and reviews all generated code and documentation critically before it enters the codebase.Communicates clearly with teammates about progress, blockers, and trade-offs; asks for help early.This role follows a hybrid model, requiring in-office presence at least 1 day per weekU.S. COMPENSATION INFORMATIONCompensation for this role includes base salary, annual discretionary performance bonus, 401(k) plan with an annual employer contribution based on years of service and Bain’s best in class benefits package (details listed below).Some local governments in the United States require a good-faith, reasonable salary range be included in job postings for open roles. The estimated annualized compensation for this role is as follows:In Atlanta, the good-faith, reasonable annualized full-time salary range for this role is between $72,000 - $86,500In Texas, the good-faith, reasonable annualized full-time salary range for this role is between $75,750 - $90,750In Chicago, the good-faith, reasonable annualized full-time salary range for this role is between $79,500 - $95,250Placement within these ranges will vary based on factors such as experience, education, training, and skill level.Compensation also includes a discretionary annual performance bonus, 401(k) plan with employer contribution, and Bain’s best-in-class benefits—including full premium coverage for medical, dental, and vision, generous paid time off, and more.Annual discretionary performance bonus This role may also be eligible for other elements of discretionary compensation4.5% 401(k) company contribution, which increases after 3 years of service and is 100% vested upon start dateBain & Company's comprehensive benefits and wellness program is designed to help employees achieve personal independence, protection and stability in the areas most important to you and your family.Bain pays 100% individual employee premiums for medical, dental and vision programs, offering one of the most comprehensive medical plans for employees without impacting your paycheckGenerous paid time off, including parental leave, sick leave and paid holidaysFully vested 401(k) company contributionPaid Life and Long-Term Disability insuranceWHAT MAKES US A GREAT PLACE TO WORKWe are proud to be consistently recognized as one of the world’s best places to work. We are currently the top ranked consulting firm on Glassdoor’s Best Places to Work list and have earned the #1 overall spot a record seven times.Extraordinary teams are at the heart of our business strategy, but these don’t happen by chance. They require intentional focus on bringing together a broad set of backgrounds, cultures, experiences, perspectives, and skills in a supportive and inclusive work environment. We hire people with exceptional talent and create an environment in which every individual can thrive professionally and personally.WHO YOU’LL WORK WITHAs the premier consulting partner for the private equity industry, Bain's PEG boasts a global practice that is over three times larger than any competitor. Our network of over 1,000 professionals supports private equity and institutional investor clients through every stage of the investment life cycle, from deal generation and due diligence to portfolio value creation and exit planning.Bain & Company is developing a suite of cutting-edge data and software solutions designed to revolutionize how the private equity industry uses data for investment insights and decision-making. The PEG Innovation team's mission is to create analytical solutions for Bain clients, teams, and the broader institutional investor space using proprietary software and data products. This includes the development, commercialization, and daily management of Bain's proprietary datasets, data, and software businesses.WHERE YOU’LL FIT WITHIN THE TEAMAI/ML Engineers on the Diligence Platform build and maintain the data, feature, and retrieval pipelines that power production RAG and ML systems. You work under the guidance of Senior ML Engineers and the Engineering Manager to implement and operate components of the ingestion, embedding, and retrieval stack, ship well-tested production code, and grow your ownership of these systems over time. You partner with Data Engineers, Data Scientists, and the Agent / AI squad on defined pieces of larger pipelines and RAG workstreams, and you build the habits, tooling fluency, and production judgment expected of a Senior ML Engineer. This is a hands-on, growth-oriented engineering role: you are expected to ship reliable, observable code from your first weeks, and to take on increasing ownership as your track record builds.WHAT YOU'LL DOCore ML and Data Pipeline Engineering (65%) Implement and maintain components of production data and ML pipelines: ingestion jobs, feature and embedding pipelines, and Celery-based workers, under the direction of senior engineers.Build and support pieces of the RAG and retrieval stack: chunking, embedding calls, indexing into pgvector, and basic retrieval and re-ranking logic, following established patterns.Write production-quality Python: type hints, tests, and linting to the team's standards, with code reviewed by senior engineers before merge.Instrument the pipelines and services you own with structured logs and metrics, and help build the dashboards and alerts that make issues visible.Reproduce, triage, and fix bugs in pipeline and serving code, escalating ambiguous or high-severity issues to senior engineers.Collaboration and Support (25%)Partner with Data Engineers, Data Scientists, and the Agent / AI squad on defined tasks within larger pipeline, retrieval, and evaluation workstreams.Contribute test cases and sample data to evaluation harnesses and golden datasets, under the direction of senior engineers.Participate in design reviews and code reviews, both as reviewer and reviewee, building judgment about production trade-offs.Keep runbooks, READMEs, and pipeline documentation current as you build and change the systems you touch.Other (10%):Use AI coding assistants to accelerate scaffolding and boilerplate, and review generated code against team standards before committing.Use LLMs to draft documentation and status notes; validate and refine outputs before sharing them.Take on interviewing and hiring-loop participation as your experience grows.ABOUT YOUBachelor's degree in Computer Science, Engineering, Machine Learning, Data Science, Statistics, or a related field (or equivalent practical experience).2+ years of experience building software, data, or ML systems, ideally including some exposure to production pipelines or services.Exposure to model deployment, serving, or monitoring is a plus.Experience working with structured feedback and code review, and a track record of improving code quality over time.Experience collaborating with Data Engineers, Data Scientists, or the Agent / AI squad to ship features that depend on retrieval or ML outputs.Comfort with Python as a primary language; exposure to a modern cloud environment (Databricks, Azure, or AWS) is a plus.Demonstrated ability to take a well-scoped task from specification to a tested, reviewed implementation with limited supervision.ML engineering / LLMOpsWorking knowledge of Python for data and ML workloads: type hints, Pydantic, pytest, Ruff, with production-quality pipeline and serving code that would pass a code review.Familiarity with MLflow concepts: experiment tracking, model registry, and promotion workflows.Exposure to LLMOps concepts: prompt versioning, model gateways (e.g., Portkey), and inference orchestration frameworks (LangChain, LlamaIndex, or equivalent).Good understanding of model-serving concepts: latency, throughput, and batching, even without direct production ownership yet.RAG pipeline building blocks: chunking strategies, embeddings, and vector stores such as pgvector; able to contribute to indexing and retrieval jobs under senior guidance.Understanding of model and pipeline evaluation basics: what a golden dataset is, and why regression gates matter in CI.Docker: comfortable containerising pipeline or serving code and running it locally for testing.Git: confident with PR-based workflows.Generative AI and agentic systemsContributes to inference and retrieval services that feed agent workflows as structured tool responses.Supports RAG quality work: helps build and run recall and precision checks against defined benchmarks.Exposure to LLM-as-judge evaluation patterns, even if applied under senior-engineer direction.GeneralTreats testing, observability, and documentation as part of the job, not an afterthought, even on smaller tasks.Raises questions and surfaces uncertainty early rather than guessing silently on ambiguous requirements.Uses AI tooling to move faster, and reviews all generated code and documentation critically before it enters the codebase.Communicates clearly with teammates about progress, blockers, and trade-offs; asks for help early.This role follows a hybrid model, requiring in-office presence at least 1 day per weekU.S. COMPENSATION INFORMATIONCompensation for this role includes base salary, annual discretionary performance bonus, 401(k) plan with an annual employer contribution based on years of service and Bain’s best in class benefits package (details listed below).Some local governments in the United States require a good-faith, reasonable salary range be included in job postings for open roles. The estimated annualized compensation for this role is as follows:In Atlanta, the good-faith, reasonable annualized full-time salary range for this role is between $72,000 - $86,500In Texas, the good-faith, reasonable annualized full-time salary range for this role is between $75,750 - $90,750In Chicago, the good-faith, reasonable annualized full-time salary range for this role is between $79,500 - $95,250Placement within these ranges will vary based on factors such as experience, education, training, and skill level.Compensation also includes a discretionary annual performance bonus, 401(k) plan with employer contribution, and Bain’s best-in-class benefits—including full premium coverage for medical, dental, and vision, generous paid time off, and more.Annual discretionary performance bonus This role may also be eligible for other elements of discretionary compensation4.5% 401(k) company contribution, which increases after 3 years of service and is 100% vested upon start dateBain & Company's comprehensive benefits and wellness program is designed to help employees achieve personal independence, protection and stability in the areas most important to you and your family.Bain pays 100% individual employee premiums for medical, dental and vision programs, offering one of the most comprehensive medical plans for employees without impacting your paycheckGenerous paid time off, including parental leave, sick leave and paid holidaysFully vested 401(k) company contributionPaid Life and Long-Term Disability insurance

WHAT MAKES US A GREAT PLACE TO WORK

WHAT MAKES US A GREAT PLACE TO WORKWHAT MAKES US A GREAT PLACE TO WORKWHAT MAKES US A GREAT PLACE TO WORK

We are proud to be consistently recognized as one of the world’s best places to work. We are currently the top ranked consulting firm on Glassdoor’s Best Places to Work list and have earned the #1 overall spot a record seven times.

We are proud to be consistently recognized as one of the world’s best places to work. We are currently the top ranked consulting firm on Glassdoor’s Best Places to Work list and have earned the #1 overall spot a record seven times.

Extraordinary teams are at the heart of our business strategy, but these don’t happen by chance. They require intentional focus on bringing together a broad set of backgrounds, cultures, experiences, perspectives, and skills in a supportive and inclusive work environment. We hire people with exceptional talent and create an environment in which every individual can thrive professionally and personally.WHO YOU’LL WORK WITH

Extraordinary teams are at the heart of our business strategy, but these don’t happen by chance. They require intentional focus on bringing together a broad set of backgrounds, cultures, experiences, perspectives, and skills in a supportive and inclusive work environment. We hire people with exceptional talent and create an environment in which every individual can thrive professionally and personally.WHO YOU’LL WORK WITHWHO YOU’LL WORK WITH

As the premier consulting partner for the private equity industry, Bain's PEG boasts a global practice that is over three times larger than any competitor. Our network of over 1,000 professionals supports private equity and institutional investor clients through every stage of the investment life cycle, from deal generation and due diligence to portfolio value creation and exit planning.

As the premier consulting partner for the private equity industry, Bain's PEG boasts a global practice that is over three times larger than any competitor. Our network of over 1,000 professionals supports private equity and institutional investor clients through every stage of the investment life cycle, from deal generation and due diligence to portfolio value creation and exit planning.

Bain & Company is developing a suite of cutting-edge data and software solutions designed to revolutionize how the private equity industry uses data for investment insights and decision-making.

Bain & Company is developing a suite of cutting-edge data and software solutions designed to revolutionize how the private equity industry uses data for investment insights and decision-making.

The PEG Innovation team's mission is to create analytical solutions for Bain clients, teams, and the broader institutional investor space using proprietary software and data products. This includes the development, commercialization, and daily management of Bain's proprietary datasets, data, and software businesses.

The PEG Innovation team's mission is to create analytical solutions for Bain clients, teams, and the broader institutional investor space using proprietary software and data products. This includes the development, commercialization, and daily management of Bain's proprietary datasets, data, and software businesses.

WHERE YOU’LL FIT WITHIN THE TEAM

WHERE YOU’LL FIT WITHIN THE TEAMWHERE YOU’LL FIT WITHIN THE TEAM

AI/ML Engineers on the Diligence Platform build and maintain the data, feature, and retrieval pipelines that power production RAG and ML systems. You work under the guidance of Senior ML Engineers and the Engineering Manager to implement and operate components of the ingestion, embedding, and retrieval stack, ship well-tested production code, and grow your ownership of these systems over time. You partner with Data Engineers, Data Scientists, and the Agent / AI squad on defined pieces of larger pipelines and RAG workstreams, and you build the habits, tooling fluency, and production judgment expected of a Senior ML Engineer. This is a hands-on, growth-oriented engineering role: you are expected to ship reliable, observable code from your first weeks, and to take on increasing ownership as your track record builds.

AI/ML Engineers on the Diligence Platform build and maintain the data, feature, and retrieval pipelines that power production RAG and ML systems. You work under the guidance of Senior ML Engineers and the Engineering Manager to implement and operate components of the ingestion, embedding, and retrieval stack, ship well-tested production code, and grow your ownership of these systems over time. You partner with Data Engineers, Data Scientists, and the Agent / AI squad on defined pieces of larger pipelines and RAG workstreams, and you build the habits, tooling fluency, and production judgment expected of a Senior ML Engineer. This is a hands-on, growth-oriented engineering role: you are expected to ship reliable, observable code from your first weeks, and to take on increasing ownership as your track record builds.

WHAT YOU'LL DO

WHAT YOU'LL DOWHAT YOU'LL DO

Core ML and Data Pipeline Engineering (65%)

Core ML and Data Pipeline Engineering (65%)Core ML and Data Pipeline Engineering (65%)

• Implement and maintain components of production data and ML pipelines: ingestion jobs, feature and embedding pipelines, and Celery-based workers, under the direction of senior engineers.

Implement and maintain components of production data and ML pipelines: ingestion jobs, feature and embedding pipelines, and Celery-based workers, under the direction of senior engineers.

• Build and support pieces of the RAG and retrieval stack: chunking, embedding calls, indexing into pgvector, and basic retrieval and re-ranking logic, following established patterns.

Build and support pieces of the RAG and retrieval stack: chunking, embedding calls, indexing into pgvector, and basic retrieval and re-ranking logic, following established patterns.

• Write production-quality Python: type hints, tests, and linting to the team's standards, with code reviewed by senior engineers before merge.

Write production-quality Python: type hints, tests, and linting to the team's standards, with code reviewed by senior engineers before merge.

• Instrument the pipelines and services you own with structured logs and metrics, and help build the dashboards and alerts that make issues visible.

Instrument the pipelines and services you own with structured logs and metrics, and help build the dashboards and alerts that make issues visible.

• Reproduce, triage, and fix bugs in pipeline and serving code, escalating ambiguous or high-severity issues to senior engineers.

Reproduce, triage, and fix bugs in pipeline and serving code, escalating ambiguous or high-severity issues to senior engineers.

Collaboration and Support (25%)

Collaboration and Support (25%)Collaboration and Support (25%)

• Partner with Data Engineers, Data Scientists, and the Agent / AI squad on defined tasks within larger pipeline, retrieval, and evaluation workstreams.

Partner with Data Engineers, Data Scientists, and the Agent / AI squad on defined tasks within larger pipeline, retrieval, and evaluation workstreams.

• Contribute test cases and sample data to evaluation harnesses and golden datasets, under the direction of senior engineers.

Contribute test cases and sample data to evaluation harnesses and golden datasets, under the direction of senior engineers.

• Participate in design reviews and code reviews, both as reviewer and reviewee, building judgment about production trade-offs.

Participate in design reviews and code reviews, both as reviewer and reviewee, building judgment about production trade-offs.

• Keep runbooks, READMEs, and pipeline documentation current as you build and change the systems you touch.

Keep runbooks, READMEs, and pipeline documentation current as you build and change the systems you touch.

Other (10%):

Other (10%):Other (10%):

• Use AI coding assistants to accelerate scaffolding and boilerplate, and review generated code against team standards before committing.

Use AI coding assistants to accelerate scaffolding and boilerplate, and review generated code against team standards before committing.

• Use LLMs to draft documentation and status notes; validate and refine outputs before sharing them.

Use LLMs to draft documentation and status notes; validate and refine outputs before sharing them.

• Take on interviewing and hiring-loop participation as your experience grows.

Take on interviewing and hiring-loop participation as your experience grows.

ABOUT YOU

ABOUT YOUABOUT YOU

• Bachelor's degree in Computer Science, Engineering, Machine Learning, Data Science, Statistics, or a related field (or equivalent practical experience).

Bachelor's degree in Computer Science, Engineering, Machine Learning, Data Science, Statistics, or a related field (or equivalent practical experience).

• 2+ years of experience building software, data, or ML systems, ideally including some exposure to production pipelines or services.

2+ years of experience building software, data, or ML systems, ideally including some exposure to production pipelines or services.

• Exposure to model deployment, serving, or monitoring is a plus.

Exposure to model deployment, serving, or monitoring is a plus.

• Experience working with structured feedback and code review, and a track record of improving code quality over time.

Experience working with structured feedback and code review, and a track record of improving code quality over time.

• Experience collaborating with Data Engineers, Data Scientists, or the Agent / AI squad to ship features that depend on retrieval or ML outputs.

Experience collaborating with Data Engineers, Data Scientists, or the Agent / AI squad to ship features that depend on retrieval or ML outputs.

• Comfort with Python as a primary language; exposure to a modern cloud environment (Databricks, Azure, or AWS) is a plus.

Comfort with Python as a primary language; exposure to a modern cloud environment (Databricks, Azure, or AWS) is a plus.

• Demonstrated ability to take a well-scoped task from specification to a tested, reviewed implementation with limited supervision.

Demonstrated ability to take a well-scoped task from specification to a tested, reviewed implementation with limited supervision.

ML engineering / LLMOps

ML engineering / LLMOpsML engineering / LLMOps

• Working knowledge of Python for data and ML workloads: type hints, Pydantic, pytest, Ruff, with production-quality pipeline and serving code that would pass a code review.

Working knowledge of Python for data and ML workloads: type hints, Pydantic, pytest, Ruff, with production-quality pipeline and serving code that would pass a code review.

• Familiarity with MLflow concepts: experiment tracking, model registry, and promotion workflows.

Familiarity with MLflow concepts: experiment tracking, model registry, and promotion workflows.

• Exposure to LLMOps concepts: prompt versioning, model gateways (e.g., Portkey), and inference orchestration frameworks (LangChain, LlamaIndex, or equivalent).

Exposure to LLMOps concepts: prompt versioning, model gateways (e.g., Portkey), and inference orchestration frameworks (LangChain, LlamaIndex, or equivalent).

• Good understanding of model-serving concepts: latency, throughput, and batching, even without direct production ownership yet.

Good understanding of model-serving concepts: latency, throughput, and batching, even without direct production ownership yet.

• RAG pipeline building blocks: chunking strategies, embeddings, and vector stores such as pgvector; able to contribute to indexing and retrieval jobs under senior guidance.

RAG pipeline building blocks: chunking strategies, embeddings, and vector stores such as pgvector; able to contribute to indexing and retrieval jobs under senior guidance.

• Understanding of model and pipeline evaluation basics: what a golden dataset is, and why regression gates matter in CI.

Understanding of model and pipeline evaluation basics: what a golden dataset is, and why regression gates matter in CI.

• Docker: comfortable containerising pipeline or serving code and running it locally for testing.

Docker: comfortable containerising pipeline or serving code and running it locally for testing.

• Git: confident with PR-based workflows.

Git: confident with PR-based workflows.

Generative AI and agentic systems

Generative AI and agentic systemsGenerative AI and agentic systems

• Contributes to inference and retrieval services that feed agent workflows as structured tool responses.

Contributes to inference and retrieval services that feed agent workflows as structured tool responses.

• Supports RAG quality work: helps build and run recall and precision checks against defined benchmarks.

Supports RAG quality work: helps build and run recall and precision checks against defined benchmarks.

• Exposure to LLM-as-judge evaluation patterns, even if applied under senior-engineer direction.

Exposure to LLM-as-judge evaluation patterns, even if applied under senior-engineer direction.

General

GeneralGeneral

• Treats testing, observability, and documentation as part of the job, not an afterthought, even on smaller tasks.

Treats testing, observability, and documentation as part of the job, not an afterthought, even on smaller tasks.

• Raises questions and surfaces uncertainty early rather than guessing silently on ambiguous requirements.

Raises questions and surfaces uncertainty early rather than guessing silently on ambiguous requirements.

• Uses AI tooling to move faster, and reviews all generated code and documentation critically before it enters the codebase.

Uses AI tooling to move faster, and reviews all generated code and documentation critically before it enters the codebase.

• Communicates clearly with teammates about progress, blockers, and trade-offs; asks for help early.

Communicates clearly with teammates about progress, blockers, and trade-offs; asks for help early.

• This role follows a hybrid model, requiring in-office presence at least 1 day per week

This role follows a hybrid model, requiring in-office presence at least 1 day per week

U.S. COMPENSATION INFORMATION

U.S. COMPENSATION INFORMATIONU.S. COMPENSATION INFORMATION

Compensation for this role includes base salary, annual discretionary performance bonus, 401(k) plan with an annual employer contribution based on years of service and Bain’s best in class benefits package (details listed below).

Compensation for this role includes base salary, annual discretionary performance bonus, 401(k) plan with an annual employer contribution based on years of service and Bain’s best in class benefits package (details listed below).

Some local governments in the United States require a good-faith, reasonable salary range be included in job postings for open roles. The estimated annualized compensation for this role is as follows:

Some local governments in the United States require a good-faith, reasonable salary range be included in job postings for open roles. The estimated annualized compensation for this role is as follows:

In Atlanta, the good-faith, reasonable annualized full-time salary range for this role is between $72,000 - $86,500

In Atlanta, the good-faith, reasonable annualized full-time salary range for this role is between $72,000 - $86,500

In Texas, the good-faith, reasonable annualized full-time salary range for this role is between $75,750 - $90,750

In Texas, the good-faith, reasonable annualized full-time salary range for this role is between $75,750 - $90,750

In Chicago, the good-faith, reasonable annualized full-time salary range for this role is between $79,500 - $95,250

In Chicago, the good-faith, reasonable annualized full-time salary range for this role is between $79,500 - $95,250

Placement within these ranges will vary based on factors such as experience, education, training, and skill level.

Placement within these ranges will vary based on factors such as experience, education, training, and skill level.

Compensation also includes a discretionary annual performance bonus, 401(k) plan with employer contribution, and Bain’s best-in-class benefits—including full premium coverage for medical, dental, and vision, generous paid time off, and more.

Compensation also includes a discretionary annual performance bonus, 401(k) plan with employer contribution, and Bain’s best-in-class benefits—including full premium coverage for medical, dental, and vision, generous paid time off, and more.

Annual discretionary performance bonus

Annual discretionary performance bonus

This role may also be eligible for other elements of discretionary compensation

This role may also be eligible for other elements of discretionary compensation

4.5% 401(k) company contribution, which increases after 3 years of service and is 100% vested upon start date

4.5% 401(k) company contribution, which increases after 3 years of service and is 100% vested upon start date

Bain & Company's comprehensive benefits and wellness program is designed to help employees achieve personal independence, protection and stability in the areas most important to you and your family.

Bain & Company's comprehensive benefits and wellness program is designed to help employees achieve personal independence, protection and stability in the areas most important to you and your family.

Bain pays 100% individual employee premiums for medical, dental and vision programs, offering one of the most comprehensive medical plans for employees without impacting your paycheck

Bain pays 100% individual employee premiums for medical, dental and vision programs, offering one of the most comprehensive medical plans for employees without impacting your paycheck

Generous paid time off, including parental leave, sick leave and paid holidays

Generous paid time off, including parental leave, sick leave and paid holidays

Fully vested 401(k) company contribution

Fully vested 401(k) company contribution

Paid Life and Long-Term Disability insurance

Paid Life and Long-Term Disability insurance