Senior AI Engineer (LLM & Agent Systems) — Platform

Role SummaryThis role focuses on designing and scaling reusable AI-driven workflows, particularly those powered by large language models and autonomous agents. You will build foundational components and abstractions that enable multiple internal teams to rapidly develop and deploy intelligent systems. The position emphasizes system reliability, evaluation rigor, and thoughtful tradeoffs in model and tooling selection.Core Ownership AreasDevelop reusable agent-based workflows to accelerate delivery across multiple projects.Define and maintain evaluation standards to ensure consistent model performance over time.Improve system reliability across key dimensions such as accuracy, latency, and robustness.Build shared APIs and platform components used broadly across engineering teams.Key ResponsibilitiesDesign and implement orchestration patterns for LLM-powered agents.Evaluate and select models, tools, and providers based on performance, cost, and reliability.Build testing frameworks, evaluation pipelines, and monitoring systems for AI outputs.Implement safeguards, fallback mechanisms, and cost optimization strategies.Collaborate with platform and backend engineers to integrate AI capabilities into scalable services.Identify repeatable patterns across projects and convert them into reusable platform features.RequirementsRequired ExperienceStrong background in building production-grade distributed systems or platform infrastructure.Practical experience developing and deploying LLM-based or agent-driven systems.Demonstrated ability to design for reliability, observability, and cost efficiency.High standards for code quality and system design.Nice-to-Have ExperienceFamiliarity with retrieval systems, embeddings, or context management pipelines.Experience working within regulated or security-conscious environments.Approach to WorkPrioritizes measurable quality through structured evaluation and testing.Designs systems for reuse, scalability, and clean abstraction layers.Focuses on building solutions that generalize beyond a single use case or team.Benefits- Hybrid onsite.- Incredible perks and comp package.Role SummaryThis role focuses on designing and scaling reusable AI-driven workflows, particularly those powered by large language models and autonomous agents. You will build foundational components and abstractions that enable multiple internal teams to rapidly develop and deploy intelligent systems. The position emphasizes system reliability, evaluation rigor, and thoughtful tradeoffs in model and tooling selection.Core Ownership AreasDevelop reusable agent-based workflows to accelerate delivery across multiple projects.Define and maintain evaluation standards to ensure consistent model performance over time.Improve system reliability across key dimensions such as accuracy, latency, and robustness.Build shared APIs and platform components used broadly across engineering teams.Key ResponsibilitiesDesign and implement orchestration patterns for LLM-powered agents.Evaluate and select models, tools, and providers based on performance, cost, and reliability.Build testing frameworks, evaluation pipelines, and monitoring systems for AI outputs.Implement safeguards, fallback mechanisms, and cost optimization strategies.Collaborate with platform and backend engineers to integrate AI capabilities into scalable services.Identify repeatable patterns across projects and convert them into reusable platform features.Role SummaryRole SummaryThis role focuses on designing and scaling reusable AI-driven workflows, particularly those powered by large language models and autonomous agents. You will build foundational components and abstractions that enable multiple internal teams to rapidly develop and deploy intelligent systems. The position emphasizes system reliability, evaluation rigor, and thoughtful tradeoffs in model and tooling selection.

Core Ownership Areas

Core Ownership Areas

• Develop reusable agent-based workflows to accelerate delivery across multiple projects.

Develop reusable agent-based workflows to accelerate delivery across multiple projects.

• Define and maintain evaluation standards to ensure consistent model performance over time.

Define and maintain evaluation standards to ensure consistent model performance over time.

• Improve system reliability across key dimensions such as accuracy, latency, and robustness.

Improve system reliability across key dimensions such as accuracy, latency, and robustness.

• Build shared APIs and platform components used broadly across engineering teams.

Build shared APIs and platform components used broadly across engineering teams.

Key Responsibilities

Key Responsibilities

• Design and implement orchestration patterns for LLM-powered agents.

Design and implement orchestration patterns for LLM-powered agents.

• Evaluate and select models, tools, and providers based on performance, cost, and reliability.

Evaluate and select models, tools, and providers based on performance, cost, and reliability.

• Build testing frameworks, evaluation pipelines, and monitoring systems for AI outputs.

Build testing frameworks, evaluation pipelines, and monitoring systems for AI outputs.

• Implement safeguards, fallback mechanisms, and cost optimization strategies.

Implement safeguards, fallback mechanisms, and cost optimization strategies.

• Collaborate with platform and backend engineers to integrate AI capabilities into scalable services.

Collaborate with platform and backend engineers to integrate AI capabilities into scalable services.

• Identify repeatable patterns across projects and convert them into reusable platform features.

Identify repeatable patterns across projects and convert them into reusable platform features.