Lead, AI Engineering

General Information Job Title Lead, AI Engineering Job ID 108810 Work Areas Technology & Engineering Employment Type Permanent Full-Time Location(s) Sao Paulo 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 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. About Bain AI, Insights & Solutions (AIS)Bain’s AI, Insights & Solutions (AIS) team works with clients to design and deliver AI-powered solutions that create measurable business impact. You’ll operate in multidisciplinary teams alongside Bain consultants, other experts in product, design, architecture and engineering, and client stakeholders, translating ambiguous business problems into robust AI applications that can be piloted, scaled, and adopted. The Impact You’ll HaveBain works with clients on board-level and executive priorities, helping deliver step-change results across growth, productivity, and resilience. In that context, AI is rarely a point solution. The most meaningful outcomes come from building AI as part of an integrated system that combines technology with redesigned processes, operating model changes, and adoption at scale across the organization. As an AI Engineer in AIS, you will build the technical core of these transformations and work as part of broader Bain consulting teams to move solutions from prototype to real adoption. The result is measurable impact at the company or enterprise level and, in many cases, helps clients set new performance standards for their industries.The RoleThe Lead AI Engineer will design, build, and ship generative AI systems and agentic solutions for Bain’s clients. You will contribute across the full development lifecycle — from early experimentation and prototyping through to production deployment — collaborating closely with senior engineers, product managers, and data scientists. This is a hands-on individual contributor role with growing influence on technical direction and an opportunity to begin mentoring more junior team members. You will have opportunities to work with major AI ecosystem partners through Bain’s partnerships, collaborating on real client deployments and helping shape how emerging capabilities are applied in enterprise settings. Bain offers significant learning and growth opportunities through the breadth and depth of problems we solve, the level of impact we help clients achieve, and our apprenticeship model. You will learn by doing, with support from experienced teammates, frequent feedback, and increasing responsibility over time. What You’ll DoContribute to the design, development, and deployment of end-to-end generative AI systems, including multi-agent workflows and production-grade AI applications.Build and iterate on multi-component AI pipelines, including:Retrieval-Augmented Generation (RAG) Fine-tuning and parameter-efficient tuningEmbedding generation and optimizationHybrid retrieval strategies (vector, graph, keyword)Implement reasoning, tool use, function calling, and orchestration across AI workflowsBuild and contribute to agentic systems, applying sound engineering principles around separation of concerns, memory architecture, and tool integrationContribute across the full stack: model experimentation, evaluation design, and production system deploymentBuild and maintain APIs, microservices, CI/CD pipelines, and cloud-native deployments with attention to observability and reliabilitySupport and help build GenAIOps processes for automated testing, regression evaluation, latency monitoring, and continual improvementBalance performance, safety, responsible AI principles, and cost across system design:Implement guardrails, fallbacks, red-teaming strategies, and human-in-the-loop (HITL) workflowsPartner with global ethics teams to ensure alignment with Bain’s Responsible AI standardsBuild automated evaluation suites integrating user signals, continual learning cycles, and ongoing model updatesDesign and implement evaluation frameworks covering:Hallucination rate and factual consistencyRelevance and precision/recallLatency, throughput, and system-level performanceCost tracking and efficiencyPartner closely with product, engineering, data science, ethics, and infrastructure teams to build robust, compliant AI systemsContribute technical insights and communicate findings clearly to cross-functional stakeholders and, where relevant, clientsShare knowledge with peers and support a culture of technical learning around RAG, agents, prompt engineering, and AI safety What We’re Looking For (Qualifications)3–5+ years in software engineering, ML engineering, or applied AI roles with hands-on building responsibilitiesDemonstrated experience shipping generative AI features or systems end-to-end, from prototyping through productionClear communication skills with the ability to explain technical concepts to non-technical collaborators and stakeholdersDemonstrated ability to collaborate effectively across engineering, product, and data science teams; some experience supporting or informally mentoring peers is a plusSolid prompt engineering and context engineering skills; familiarity with conversation design principlesWorking knowledge of evaluation design, experimentation frameworks, and data labeling strategies for LLM applicationsExperience with:RAG architectures (vector-based retrieval; exposure to hybrid or graph-based approaches is a plus)Agentic patterns (tool use, routing, memory management; multi-agent systems experience is a plus)ReAct, RLAIF, and other HITL + feedback loops.AI-Specific Tools & Frameworks - Orchestration frameworks, Vector and graph databases and Model + API ecosystemsStrong background in system design, architecture, and production-grade deploymentFamiliarity with cost and latency tradeoffs when working with LLM workloadsComfort operating in high-ambiguity environments with collaborative cross-functional teamsEagerness to grow into a technical leadership role and support junior team membersExperience in client-facing consulting or enterprise transformation environments is a strong plus Working Model & TravelThis role requires a minimum of three days per week working together in person, either at a client location or at your Bain home office.Travel is required beyond your home office / primary working location. Frequency and destination vary by project needs. Go backGeneral Information Job Title Lead, AI Engineering Job ID 108810 Work Areas Technology & Engineering Employment Type Permanent Full-Time Location(s) Sao Paulo 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 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. About Bain AI, Insights & Solutions (AIS)Bain’s AI, Insights & Solutions (AIS) team works with clients to design and deliver AI-powered solutions that create measurable business impact. You’ll operate in multidisciplinary teams alongside Bain consultants, other experts in product, design, architecture and engineering, and client stakeholders, translating ambiguous business problems into robust AI applications that can be piloted, scaled, and adopted. The Impact You’ll HaveBain works with clients on board-level and executive priorities, helping deliver step-change results across growth, productivity, and resilience. In that context, AI is rarely a point solution. The most meaningful outcomes come from building AI as part of an integrated system that combines technology with redesigned processes, operating model changes, and adoption at scale across the organization. As an AI Engineer in AIS, you will build the technical core of these transformations and work as part of broader Bain consulting teams to move solutions from prototype to real adoption. The result is measurable impact at the company or enterprise level and, in many cases, helps clients set new performance standards for their industries.The RoleThe Lead AI Engineer will design, build, and ship generative AI systems and agentic solutions for Bain’s clients. You will contribute across the full development lifecycle — from early experimentation and prototyping through to production deployment — collaborating closely with senior engineers, product managers, and data scientists. This is a hands-on individual contributor role with growing influence on technical direction and an opportunity to begin mentoring more junior team members. You will have opportunities to work with major AI ecosystem partners through Bain’s partnerships, collaborating on real client deployments and helping shape how emerging capabilities are applied in enterprise settings. Bain offers significant learning and growth opportunities through the breadth and depth of problems we solve, the level of impact we help clients achieve, and our apprenticeship model. You will learn by doing, with support from experienced teammates, frequent feedback, and increasing responsibility over time. What You’ll DoContribute to the design, development, and deployment of end-to-end generative AI systems, including multi-agent workflows and production-grade AI applications.Build and iterate on multi-component AI pipelines, including:Retrieval-Augmented Generation (RAG) Fine-tuning and parameter-efficient tuningEmbedding generation and optimizationHybrid retrieval strategies (vector, graph, keyword)Implement reasoning, tool use, function calling, and orchestration across AI workflowsBuild and contribute to agentic systems, applying sound engineering principles around separation of concerns, memory architecture, and tool integrationContribute across the full stack: model experimentation, evaluation design, and production system deploymentBuild and maintain APIs, microservices, CI/CD pipelines, and cloud-native deployments with attention to observability and reliabilitySupport and help build GenAIOps processes for automated testing, regression evaluation, latency monitoring, and continual improvementBalance performance, safety, responsible AI principles, and cost across system design:Implement guardrails, fallbacks, red-teaming strategies, and human-in-the-loop (HITL) workflowsPartner with global ethics teams to ensure alignment with Bain’s Responsible AI standardsBuild automated evaluation suites integrating user signals, continual learning cycles, and ongoing model updatesDesign and implement evaluation frameworks covering:Hallucination rate and factual consistencyRelevance and precision/recallLatency, throughput, and system-level performanceCost tracking and efficiencyPartner closely with product, engineering, data science, ethics, and infrastructure teams to build robust, compliant AI systemsContribute technical insights and communicate findings clearly to cross-functional stakeholders and, where relevant, clientsShare knowledge with peers and support a culture of technical learning around RAG, agents, prompt engineering, and AI safety What We’re Looking For (Qualifications)3–5+ years in software engineering, ML engineering, or applied AI roles with hands-on building responsibilitiesDemonstrated experience shipping generative AI features or systems end-to-end, from prototyping through productionClear communication skills with the ability to explain technical concepts to non-technical collaborators and stakeholdersDemonstrated ability to collaborate effectively across engineering, product, and data science teams; some experience supporting or informally mentoring peers is a plusSolid prompt engineering and context engineering skills; familiarity with conversation design principlesWorking knowledge of evaluation design, experimentation frameworks, and data labeling strategies for LLM applicationsExperience with:RAG architectures (vector-based retrieval; exposure to hybrid or graph-based approaches is a plus)Agentic patterns (tool use, routing, memory management; multi-agent systems experience is a plus)ReAct, RLAIF, and other HITL + feedback loops.AI-Specific Tools & Frameworks - Orchestration frameworks, Vector and graph databases and Model + API ecosystemsStrong background in system design, architecture, and production-grade deploymentFamiliarity with cost and latency tradeoffs when working with LLM workloadsComfort operating in high-ambiguity environments with collaborative cross-functional teamsEagerness to grow into a technical leadership role and support junior team membersExperience in client-facing consulting or enterprise transformation environments is a strong plus Working Model & TravelThis role requires a minimum of three days per week working together in person, either at a client location or at your Bain home office.Travel is required beyond your home office / primary working location. Frequency and destination vary by project needs.General InformationGeneral Information

General Information

Job Title Lead, AI Engineering Job ID 108810 Work Areas Technology & Engineering Employment Type Permanent Full-Time Location(s) Sao PauloJob Title Lead, AI Engineering Job ID 108810 Work Areas Technology & Engineering Employment Type Permanent Full-Time Location(s) Sao PauloJob Title Lead, AI EngineeringJob TitleLead, AI EngineeringJob ID 108810Job ID108810Work Areas Technology & EngineeringWork AreasTechnology & EngineeringEmployment Type Permanent Full-TimeEmployment TypePermanent Full-TimeLocation(s) Sao PauloLocation(s)Sao PauloDescription & 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 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. About Bain AI, Insights & Solutions (AIS)Bain’s AI, Insights & Solutions (AIS) team works with clients to design and deliver AI-powered solutions that create measurable business impact. You’ll operate in multidisciplinary teams alongside Bain consultants, other experts in product, design, architecture and engineering, and client stakeholders, translating ambiguous business problems into robust AI applications that can be piloted, scaled, and adopted. The Impact You’ll HaveBain works with clients on board-level and executive priorities, helping deliver step-change results across growth, productivity, and resilience. In that context, AI is rarely a point solution. The most meaningful outcomes come from building AI as part of an integrated system that combines technology with redesigned processes, operating model changes, and adoption at scale across the organization. As an AI Engineer in AIS, you will build the technical core of these transformations and work as part of broader Bain consulting teams to move solutions from prototype to real adoption. The result is measurable impact at the company or enterprise level and, in many cases, helps clients set new performance standards for their industries.The RoleThe Lead AI Engineer will design, build, and ship generative AI systems and agentic solutions for Bain’s clients. You will contribute across the full development lifecycle — from early experimentation and prototyping through to production deployment — collaborating closely with senior engineers, product managers, and data scientists. This is a hands-on individual contributor role with growing influence on technical direction and an opportunity to begin mentoring more junior team members. You will have opportunities to work with major AI ecosystem partners through Bain’s partnerships, collaborating on real client deployments and helping shape how emerging capabilities are applied in enterprise settings. Bain offers significant learning and growth opportunities through the breadth and depth of problems we solve, the level of impact we help clients achieve, and our apprenticeship model. You will learn by doing, with support from experienced teammates, frequent feedback, and increasing responsibility over time. What You’ll DoContribute to the design, development, and deployment of end-to-end generative AI systems, including multi-agent workflows and production-grade AI applications.Build and iterate on multi-component AI pipelines, including:Retrieval-Augmented Generation (RAG) Fine-tuning and parameter-efficient tuningEmbedding generation and optimizationHybrid retrieval strategies (vector, graph, keyword)Implement reasoning, tool use, function calling, and orchestration across AI workflowsBuild and contribute to agentic systems, applying sound engineering principles around separation of concerns, memory architecture, and tool integrationContribute across the full stack: model experimentation, evaluation design, and production system deploymentBuild and maintain APIs, microservices, CI/CD pipelines, and cloud-native deployments with attention to observability and reliabilitySupport and help build GenAIOps processes for automated testing, regression evaluation, latency monitoring, and continual improvementBalance performance, safety, responsible AI principles, and cost across system design:Implement guardrails, fallbacks, red-teaming strategies, and human-in-the-loop (HITL) workflowsPartner with global ethics teams to ensure alignment with Bain’s Responsible AI standardsBuild automated evaluation suites integrating user signals, continual learning cycles, and ongoing model updatesDesign and implement evaluation frameworks covering:Hallucination rate and factual consistencyRelevance and precision/recallLatency, throughput, and system-level performanceCost tracking and efficiencyPartner closely with product, engineering, data science, ethics, and infrastructure teams to build robust, compliant AI systemsContribute technical insights and communicate findings clearly to cross-functional stakeholders and, where relevant, clientsShare knowledge with peers and support a culture of technical learning around RAG, agents, prompt engineering, and AI safety What We’re Looking For (Qualifications)3–5+ years in software engineering, ML engineering, or applied AI roles with hands-on building responsibilitiesDemonstrated experience shipping generative AI features or systems end-to-end, from prototyping through productionClear communication skills with the ability to explain technical concepts to non-technical collaborators and stakeholdersDemonstrated ability to collaborate effectively across engineering, product, and data science teams; some experience supporting or informally mentoring peers is a plusSolid prompt engineering and context engineering skills; familiarity with conversation design principlesWorking knowledge of evaluation design, experimentation frameworks, and data labeling strategies for LLM applicationsExperience with:RAG architectures (vector-based retrieval; exposure to hybrid or graph-based approaches is a plus)Agentic patterns (tool use, routing, memory management; multi-agent systems experience is a plus)ReAct, RLAIF, and other HITL + feedback loops.AI-Specific Tools & Frameworks - Orchestration frameworks, Vector and graph databases and Model + API ecosystemsStrong background in system design, architecture, and production-grade deploymentFamiliarity with cost and latency tradeoffs when working with LLM workloadsComfort operating in high-ambiguity environments with collaborative cross-functional teamsEagerness to grow into a technical leadership role and support junior team membersExperience in client-facing consulting or enterprise transformation environments is a strong plus Working Model & TravelThis role requires a minimum of three days per week working together in person, either at a client location or at your Bain home office.Travel is required beyond your home office / primary working location. Frequency and destination vary by project needs.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 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. About Bain AI, Insights & Solutions (AIS)Bain’s AI, Insights & Solutions (AIS) team works with clients to design and deliver AI-powered solutions that create measurable business impact. You’ll operate in multidisciplinary teams alongside Bain consultants, other experts in product, design, architecture and engineering, and client stakeholders, translating ambiguous business problems into robust AI applications that can be piloted, scaled, and adopted. The Impact You’ll HaveBain works with clients on board-level and executive priorities, helping deliver step-change results across growth, productivity, and resilience. In that context, AI is rarely a point solution. The most meaningful outcomes come from building AI as part of an integrated system that combines technology with redesigned processes, operating model changes, and adoption at scale across the organization. As an AI Engineer in AIS, you will build the technical core of these transformations and work as part of broader Bain consulting teams to move solutions from prototype to real adoption. The result is measurable impact at the company or enterprise level and, in many cases, helps clients set new performance standards for their industries.The RoleThe Lead AI Engineer will design, build, and ship generative AI systems and agentic solutions for Bain’s clients. You will contribute across the full development lifecycle — from early experimentation and prototyping through to production deployment — collaborating closely with senior engineers, product managers, and data scientists. This is a hands-on individual contributor role with growing influence on technical direction and an opportunity to begin mentoring more junior team members. You will have opportunities to work with major AI ecosystem partners through Bain’s partnerships, collaborating on real client deployments and helping shape how emerging capabilities are applied in enterprise settings. Bain offers significant learning and growth opportunities through the breadth and depth of problems we solve, the level of impact we help clients achieve, and our apprenticeship model. You will learn by doing, with support from experienced teammates, frequent feedback, and increasing responsibility over time. What You’ll DoContribute to the design, development, and deployment of end-to-end generative AI systems, including multi-agent workflows and production-grade AI applications.Build and iterate on multi-component AI pipelines, including:Retrieval-Augmented Generation (RAG) Fine-tuning and parameter-efficient tuningEmbedding generation and optimizationHybrid retrieval strategies (vector, graph, keyword)Implement reasoning, tool use, function calling, and orchestration across AI workflowsBuild and contribute to agentic systems, applying sound engineering principles around separation of concerns, memory architecture, and tool integrationContribute across the full stack: model experimentation, evaluation design, and production system deploymentBuild and maintain APIs, microservices, CI/CD pipelines, and cloud-native deployments with attention to observability and reliabilitySupport and help build GenAIOps processes for automated testing, regression evaluation, latency monitoring, and continual improvementBalance performance, safety, responsible AI principles, and cost across system design:Implement guardrails, fallbacks, red-teaming strategies, and human-in-the-loop (HITL) workflowsPartner with global ethics teams to ensure alignment with Bain’s Responsible AI standardsBuild automated evaluation suites integrating user signals, continual learning cycles, and ongoing model updatesDesign and implement evaluation frameworks covering:Hallucination rate and factual consistencyRelevance and precision/recallLatency, throughput, and system-level performanceCost tracking and efficiencyPartner closely with product, engineering, data science, ethics, and infrastructure teams to build robust, compliant AI systemsContribute technical insights and communicate findings clearly to cross-functional stakeholders and, where relevant, clientsShare knowledge with peers and support a culture of technical learning around RAG, agents, prompt engineering, and AI safety What We’re Looking For (Qualifications)3–5+ years in software engineering, ML engineering, or applied AI roles with hands-on building responsibilitiesDemonstrated experience shipping generative AI features or systems end-to-end, from prototyping through productionClear communication skills with the ability to explain technical concepts to non-technical collaborators and stakeholdersDemonstrated ability to collaborate effectively across engineering, product, and data science teams; some experience supporting or informally mentoring peers is a plusSolid prompt engineering and context engineering skills; familiarity with conversation design principlesWorking knowledge of evaluation design, experimentation frameworks, and data labeling strategies for LLM applicationsExperience with:RAG architectures (vector-based retrieval; exposure to hybrid or graph-based approaches is a plus)Agentic patterns (tool use, routing, memory management; multi-agent systems experience is a plus)ReAct, RLAIF, and other HITL + feedback loops.AI-Specific Tools & Frameworks - Orchestration frameworks, Vector and graph databases and Model + API ecosystemsStrong background in system design, architecture, and production-grade deploymentFamiliarity with cost and latency tradeoffs when working with LLM workloadsComfort operating in high-ambiguity environments with collaborative cross-functional teamsEagerness to grow into a technical leadership role and support junior team membersExperience in client-facing consulting or enterprise transformation environments is a strong plus Working Model & TravelThis role requires a minimum of three days per week working together in person, either at a client location or at your Bain home office.Travel is required beyond your home office / primary working location. Frequency and destination vary by project needs.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 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. About Bain AI, Insights & Solutions (AIS)Bain’s AI, Insights & Solutions (AIS) team works with clients to design and deliver AI-powered solutions that create measurable business impact. You’ll operate in multidisciplinary teams alongside Bain consultants, other experts in product, design, architecture and engineering, and client stakeholders, translating ambiguous business problems into robust AI applications that can be piloted, scaled, and adopted. The Impact You’ll HaveBain works with clients on board-level and executive priorities, helping deliver step-change results across growth, productivity, and resilience. In that context, AI is rarely a point solution. The most meaningful outcomes come from building AI as part of an integrated system that combines technology with redesigned processes, operating model changes, and adoption at scale across the organization. As an AI Engineer in AIS, you will build the technical core of these transformations and work as part of broader Bain consulting teams to move solutions from prototype to real adoption. The result is measurable impact at the company or enterprise level and, in many cases, helps clients set new performance standards for their industries.The RoleThe Lead AI Engineer will design, build, and ship generative AI systems and agentic solutions for Bain’s clients. You will contribute across the full development lifecycle — from early experimentation and prototyping through to production deployment — collaborating closely with senior engineers, product managers, and data scientists. This is a hands-on individual contributor role with growing influence on technical direction and an opportunity to begin mentoring more junior team members. You will have opportunities to work with major AI ecosystem partners through Bain’s partnerships, collaborating on real client deployments and helping shape how emerging capabilities are applied in enterprise settings. Bain offers significant learning and growth opportunities through the breadth and depth of problems we solve, the level of impact we help clients achieve, and our apprenticeship model. You will learn by doing, with support from experienced teammates, frequent feedback, and increasing responsibility over time. What You’ll DoContribute to the design, development, and deployment of end-to-end generative AI systems, including multi-agent workflows and production-grade AI applications.Build and iterate on multi-component AI pipelines, including:Retrieval-Augmented Generation (RAG) Fine-tuning and parameter-efficient tuningEmbedding generation and optimizationHybrid retrieval strategies (vector, graph, keyword)Implement reasoning, tool use, function calling, and orchestration across AI workflowsBuild and contribute to agentic systems, applying sound engineering principles around separation of concerns, memory architecture, and tool integrationContribute across the full stack: model experimentation, evaluation design, and production system deploymentBuild and maintain APIs, microservices, CI/CD pipelines, and cloud-native deployments with attention to observability and reliabilitySupport and help build GenAIOps processes for automated testing, regression evaluation, latency monitoring, and continual improvementBalance performance, safety, responsible AI principles, and cost across system design:Implement guardrails, fallbacks, red-teaming strategies, and human-in-the-loop (HITL) workflowsPartner with global ethics teams to ensure alignment with Bain’s Responsible AI standardsBuild automated evaluation suites integrating user signals, continual learning cycles, and ongoing model updatesDesign and implement evaluation frameworks covering:Hallucination rate and factual consistencyRelevance and precision/recallLatency, throughput, and system-level performanceCost tracking and efficiencyPartner closely with product, engineering, data science, ethics, and infrastructure teams to build robust, compliant AI systemsContribute technical insights and communicate findings clearly to cross-functional stakeholders and, where relevant, clientsShare knowledge with peers and support a culture of technical learning around RAG, agents, prompt engineering, and AI safety What We’re Looking For (Qualifications)3–5+ years in software engineering, ML engineering, or applied AI roles with hands-on building responsibilitiesDemonstrated experience shipping generative AI features or systems end-to-end, from prototyping through productionClear communication skills with the ability to explain technical concepts to non-technical collaborators and stakeholdersDemonstrated ability to collaborate effectively across engineering, product, and data science teams; some experience supporting or informally mentoring peers is a plusSolid prompt engineering and context engineering skills; familiarity with conversation design principlesWorking knowledge of evaluation design, experimentation frameworks, and data labeling strategies for LLM applicationsExperience with:RAG architectures (vector-based retrieval; exposure to hybrid or graph-based approaches is a plus)Agentic patterns (tool use, routing, memory management; multi-agent systems experience is a plus)ReAct, RLAIF, and other HITL + feedback loops.AI-Specific Tools & Frameworks - Orchestration frameworks, Vector and graph databases and Model + API ecosystemsStrong background in system design, architecture, and production-grade deploymentFamiliarity with cost and latency tradeoffs when working with LLM workloadsComfort operating in high-ambiguity environments with collaborative cross-functional teamsEagerness to grow into a technical leadership role and support junior team membersExperience in client-facing consulting or enterprise transformation environments is a strong plus Working Model & TravelThis role requires a minimum of three days per week working together in person, either at a client location or at your Bain home office.Travel is required beyond your home office / primary working location. Frequency and destination vary by project needs.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 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. About Bain AI, Insights & Solutions (AIS)Bain’s AI, Insights & Solutions (AIS) team works with clients to design and deliver AI-powered solutions that create measurable business impact. You’ll operate in multidisciplinary teams alongside Bain consultants, other experts in product, design, architecture and engineering, and client stakeholders, translating ambiguous business problems into robust AI applications that can be piloted, scaled, and adopted. The Impact You’ll HaveBain works with clients on board-level and executive priorities, helping deliver step-change results across growth, productivity, and resilience. In that context, AI is rarely a point solution. The most meaningful outcomes come from building AI as part of an integrated system that combines technology with redesigned processes, operating model changes, and adoption at scale across the organization. As an AI Engineer in AIS, you will build the technical core of these transformations and work as part of broader Bain consulting teams to move solutions from prototype to real adoption. The result is measurable impact at the company or enterprise level and, in many cases, helps clients set new performance standards for their industries.The RoleThe Lead AI Engineer will design, build, and ship generative AI systems and agentic solutions for Bain’s clients. You will contribute across the full development lifecycle — from early experimentation and prototyping through to production deployment — collaborating closely with senior engineers, product managers, and data scientists. This is a hands-on individual contributor role with growing influence on technical direction and an opportunity to begin mentoring more junior team members. You will have opportunities to work with major AI ecosystem partners through Bain’s partnerships, collaborating on real client deployments and helping shape how emerging capabilities are applied in enterprise settings. Bain offers significant learning and growth opportunities through the breadth and depth of problems we solve, the level of impact we help clients achieve, and our apprenticeship model. You will learn by doing, with support from experienced teammates, frequent feedback, and increasing responsibility over time. What You’ll DoContribute to the design, development, and deployment of end-to-end generative AI systems, including multi-agent workflows and production-grade AI applications.Build and iterate on multi-component AI pipelines, including:Retrieval-Augmented Generation (RAG) Fine-tuning and parameter-efficient tuningEmbedding generation and optimizationHybrid retrieval strategies (vector, graph, keyword)Implement reasoning, tool use, function calling, and orchestration across AI workflowsBuild and contribute to agentic systems, applying sound engineering principles around separation of concerns, memory architecture, and tool integrationContribute across the full stack: model experimentation, evaluation design, and production system deploymentBuild and maintain APIs, microservices, CI/CD pipelines, and cloud-native deployments with attention to observability and reliabilitySupport and help build GenAIOps processes for automated testing, regression evaluation, latency monitoring, and continual improvementBalance performance, safety, responsible AI principles, and cost across system design:Implement guardrails, fallbacks, red-teaming strategies, and human-in-the-loop (HITL) workflowsPartner with global ethics teams to ensure alignment with Bain’s Responsible AI standardsBuild automated evaluation suites integrating user signals, continual learning cycles, and ongoing model updatesDesign and implement evaluation frameworks covering:Hallucination rate and factual consistencyRelevance and precision/recallLatency, throughput, and system-level performanceCost tracking and efficiencyPartner closely with product, engineering, data science, ethics, and infrastructure teams to build robust, compliant AI systemsContribute technical insights and communicate findings clearly to cross-functional stakeholders and, where relevant, clientsShare knowledge with peers and support a culture of technical learning around RAG, agents, prompt engineering, and AI safety What We’re Looking For (Qualifications)3–5+ years in software engineering, ML engineering, or applied AI roles with hands-on building responsibilitiesDemonstrated experience shipping generative AI features or systems end-to-end, from prototyping through productionClear communication skills with the ability to explain technical concepts to non-technical collaborators and stakeholdersDemonstrated ability to collaborate effectively across engineering, product, and data science teams; some experience supporting or informally mentoring peers is a plusSolid prompt engineering and context engineering skills; familiarity with conversation design principlesWorking knowledge of evaluation design, experimentation frameworks, and data labeling strategies for LLM applicationsExperience with:RAG architectures (vector-based retrieval; exposure to hybrid or graph-based approaches is a plus)Agentic patterns (tool use, routing, memory management; multi-agent systems experience is a plus)ReAct, RLAIF, and other HITL + feedback loops.AI-Specific Tools & Frameworks - Orchestration frameworks, Vector and graph databases and Model + API ecosystemsStrong background in system design, architecture, and production-grade deploymentFamiliarity with cost and latency tradeoffs when working with LLM workloadsComfort operating in high-ambiguity environments with collaborative cross-functional teamsEagerness to grow into a technical leadership role and support junior team membersExperience in client-facing consulting or enterprise transformation environments is a strong plus Working Model & TravelThis role requires a minimum of three days per week working together in person, either at a client location or at your Bain home office.Travel is required beyond your home office / primary working location. Frequency and destination vary by project needs.

What Makes Us a Great Place to Work

What 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 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.

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 onGlassdoor’s Best Places to Work listGlassdoor’s Best Places to Work listand have earned the #1 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.

About Bain AI, Insights & Solutions (AIS)

About Bain AI, Insights & Solutions (AIS)About Bain AI, Insights & Solutions (AIS)

Bain’s AI, Insights & Solutions (AIS) team works with clients to design and deliver AI-powered solutions that create measurable business impact. You’ll operate in multidisciplinary teams alongside Bain consultants, other experts in product, design, architecture and engineering, and client stakeholders, translating ambiguous business problems into robust AI applications that can be piloted, scaled, and adopted.

Bain’sAI, Insights & Solutions (AIS) teamAI, Insights & Solutions (AIS) teamworks with clients to design and deliver AI-powered solutions that create measurable business impact. You’ll operate in multidisciplinary teams alongside Bain consultants, other experts in product, design, architecture and engineering, and client stakeholders, translating ambiguous business problems into robust AI applications that can be piloted, scaled, and adopted.

The Impact You’ll Have

The Impact You’ll HaveThe Impact You’ll Have

Bain works with clients on board-level and executive priorities, helping deliver step-change results across growth, productivity, and resilience. In that context, AI is rarely a point solution. The most meaningful outcomes come from building AI as part of an integrated system that combines technology with redesigned processes, operating model changes, and adoption at scale across the organization.

Bain works with clients on board-level and executive priorities, helping deliver step-change results across growth, productivity, and resilience. In that context, AI is rarely a point solution. The most meaningful outcomes come from building AI as part of an integrated system that combines technology with redesigned processes, operating model changes, and adoption at scale across the organization.

As an AI Engineer in AIS, you will build the technical core of these transformations and work as part of broader Bain consulting teams to move solutions from prototype to real adoption. The result is measurable impact at the company or enterprise level and, in many cases, helps clients set new performance standards for their industries.

As an AI Engineer in AIS, you will build the technical core of these transformations and work as part of broader Bain consulting teams to move solutions from prototype to real adoption. The result is measurable impact at the company or enterprise level and, in many cases, helps clients set new performance standards for their industries.

The Role

The RoleThe Role

The Lead AI Engineer will design, build, and ship generative AI systems and agentic solutions for Bain’s clients. You will contribute across the full development lifecycle — from early experimentation and prototyping through to production deployment — collaborating closely with senior engineers, product managers, and data scientists. This is a hands-on individual contributor role with growing influence on technical direction and an opportunity to begin mentoring more junior team members.

The Lead AI Engineer will design, build, and ship generative AI systems and agentic solutions for Bain’s clients. You will contribute across the full development lifecycle — from early experimentation and prototyping through to production deployment — collaborating closely with senior engineers, product managers, and data scientists. This is a hands-on individual contributor role with growing influence on technical direction and an opportunity to begin mentoring more junior team members.

You will have opportunities to work with major AI ecosystem partners through Bain’s partnerships, collaborating on real client deployments and helping shape how emerging capabilities are applied in enterprise settings.

You will have opportunities to work with major AI ecosystem partners through Bain’s partnerships, collaborating on real client deployments and helping shape how emerging capabilities are applied in enterprise settings.

Bain offers significant learning and growth opportunities through the breadth and depth of problems we solve, the level of impact we help clients achieve, and our apprenticeship model. You will learn by doing, with support from experienced teammates, frequent feedback, and increasing responsibility over time.

Bain offers significant learning and growth opportunities through the breadth and depth of problems we solve, the level of impact we help clients achieve, and our apprenticeship model. You will learn by doing, with support from experienced teammates, frequent feedback, and increasing responsibility over time.

What You’ll Do

What You’ll DoWhat You’ll Do

• Contribute to the design, development, and deployment of end-to-end generative AI systems, including multi-agent workflows and production-grade AI applications.

Contribute to the design, development, and deployment of end-to-end generative AI systems, including multi-agent workflows and production-grade AI applications.

• Build and iterate on multi-component AI pipelines, including:Retrieval-Augmented Generation (RAG) Fine-tuning and parameter-efficient tuningEmbedding generation and optimizationHybrid retrieval strategies (vector, graph, keyword)

Build and iterate on multi-component AI pipelines, including:

• Retrieval-Augmented Generation (RAG)

Retrieval-Augmented Generation (RAG)

• Fine-tuning and parameter-efficient tuning

Fine-tuning and parameter-efficient tuning

• Embedding generation and optimization

Embedding generation and optimization

• Hybrid retrieval strategies (vector, graph, keyword)

Hybrid retrieval strategies (vector, graph, keyword)

• Implement reasoning, tool use, function calling, and orchestration across AI workflows

Implement reasoning, tool use, function calling, and orchestration across AI workflows

• Build and contribute to agentic systems, applying sound engineering principles around separation of concerns, memory architecture, and tool integration

Build and contribute to agentic systems, applying sound engineering principles around separation of concerns, memory architecture, and tool integration

• Contribute across the full stack: model experimentation, evaluation design, and production system deployment

Contribute across the full stack: model experimentation, evaluation design, and production system deployment

• Build and maintain APIs, microservices, CI/CD pipelines, and cloud-native deployments with attention to observability and reliability

Build and maintain APIs, microservices, CI/CD pipelines, and cloud-native deployments with attention to observability and reliability

• Support and help build GenAIOps processes for automated testing, regression evaluation, latency monitoring, and continual improvement

Support and help build GenAIOps processes for automated testing, regression evaluation, latency monitoring, and continual improvement

• Balance performance, safety, responsible AI principles, and cost across system design:Implement guardrails, fallbacks, red-teaming strategies, and human-in-the-loop (HITL) workflowsPartner with global ethics teams to ensure alignment with Bain’s Responsible AI standards

Balance performance, safety, responsible AI principles, and cost across system design:

• Implement guardrails, fallbacks, red-teaming strategies, and human-in-the-loop (HITL) workflows

Implement guardrails, fallbacks, red-teaming strategies, and human-in-the-loop (HITL) workflows

• Partner with global ethics teams to ensure alignment with Bain’s Responsible AI standards

Partner with global ethics teams to ensure alignment with Bain’s Responsible AI standards

• Build automated evaluation suites integrating user signals, continual learning cycles, and ongoing model updates

Build automated evaluation suites integrating user signals, continual learning cycles, and ongoing model updates

• Design and implement evaluation frameworks covering:Hallucination rate and factual consistencyRelevance and precision/recallLatency, throughput, and system-level performanceCost tracking and efficiency

Design and implement evaluation frameworks covering:

• Hallucination rate and factual consistency

Hallucination rate and factual consistency

• Relevance and precision/recall

Relevance and precision/recall

• Latency, throughput, and system-level performance

Latency, throughput, and system-level performance

• Cost tracking and efficiency

Cost tracking and efficiency

• Partner closely with product, engineering, data science, ethics, and infrastructure teams to build robust, compliant AI systems

Partner closely with product, engineering, data science, ethics, and infrastructure teams to build robust, compliant AI systems

• Contribute technical insights and communicate findings clearly to cross-functional stakeholders and, where relevant, clients

Contribute technical insights and communicate findings clearly to cross-functional stakeholders and, where relevant, clients

• Share knowledge with peers and support a culture of technical learning around RAG, agents, prompt engineering, and AI safety

Share knowledge with peers and support a culture of technical learning around RAG, agents, prompt engineering, and AI safety

What We’re Looking For (Qualifications)

What We’re Looking For (Qualifications)What We’re Looking For (Qualifications)

• 3–5+ years in software engineering, ML engineering, or applied AI roles with hands-on building responsibilities

3–5+ years in software engineering, ML engineering, or applied AI roles with hands-on building responsibilities

• Demonstrated experience shipping generative AI features or systems end-to-end, from prototyping through production

Demonstrated experience shipping generative AI features or systems end-to-end, from prototyping through production

• Clear communication skills with the ability to explain technical concepts to non-technical collaborators and stakeholders

Clear communication skills with the ability to explain technical concepts to non-technical collaborators and stakeholders

• Demonstrated ability to collaborate effectively across engineering, product, and data science teams; some experience supporting or informally mentoring peers is a plus

Demonstrated ability to collaborate effectively across engineering, product, and data science teams; some experience supporting or informally mentoring peers is a plus

• Solid prompt engineering and context engineering skills; familiarity with conversation design principles

Solid prompt engineering and context engineering skills; familiarity with conversation design principles

• Working knowledge of evaluation design, experimentation frameworks, and data labeling strategies for LLM applications

Working knowledge of evaluation design, experimentation frameworks, and data labeling strategies for LLM applications

• Experience with:RAG architectures (vector-based retrieval; exposure to hybrid or graph-based approaches is a plus)Agentic patterns (tool use, routing, memory management; multi-agent systems experience is a plus)ReAct, RLAIF, and other HITL + feedback loops.AI-Specific Tools & Frameworks - Orchestration frameworks, Vector and graph databases and Model + API ecosystems

Experience with:

• RAG architectures (vector-based retrieval; exposure to hybrid or graph-based approaches is a plus)

RAG architectures (vector-based retrieval; exposure to hybrid or graph-based approaches is a plus)

• Agentic patterns (tool use, routing, memory management; multi-agent systems experience is a plus)

Agentic patterns (tool use, routing, memory management; multi-agent systems experience is a plus)

• ReAct, RLAIF, and other HITL + feedback loops.

ReAct, RLAIF, and other HITL + feedback loops.

• AI-Specific Tools & Frameworks - Orchestration frameworks, Vector and graph databases and Model + API ecosystems

AI-Specific Tools & Frameworks - Orchestration frameworks, Vector and graph databases and Model + API ecosystems

• Strong background in system design, architecture, and production-grade deployment

Strong background in system design, architecture, and production-grade deployment

• Familiarity with cost and latency tradeoffs when working with LLM workloads

Familiarity with cost and latency tradeoffs when working with LLM workloads

• Comfort operating in high-ambiguity environments with collaborative cross-functional teams

Comfort operating in high-ambiguity environments with collaborative cross-functional teams

• Eagerness to grow into a technical leadership role and support junior team members

Eagerness to grow into a technical leadership role and support junior team members

• Experience in client-facing consulting or enterprise transformation environments is a strong plus

Experience in client-facing consulting or enterprise transformation environments is a strong plus

Working Model & Travel

Working Model & TravelWorking Model & Travel

• This role requires a minimum of three days per week working together in person, either at a client location or at your Bain home office.

This role requires a minimum of three days per week working together in person, either at a client location or at your Bain home office.

• Travel is required beyond your home office / primary working location. Frequency and destination vary by project needs.

Travel is required beyond your home office / primary working location. Frequency and destination vary by project needs.