Principal AI Engineer

Key Responsibilities

• Design, build, and deploy autonomous multi-agent workflows using orchestration frameworks such as LangGraph, CrewAI, Autogen, or similar, including complex state machines with conditional routing, parallel execution, and error recovery patterns.
• Architect graph-based agent workflows with 10+ nodes involving agent collaboration, task decomposition, and sequential/parallel execution across multiple business domains.
• Develop and maintain reusable agent node libraries, extensible platform patterns, versioning strategies, and testing frameworks (unit, integration, and end-to-end) for agent workflows.
• Build production-grade FastAPI applications with async I/O patterns, integrating PostgreSQL, Redis, and external enterprise services.
• Implement real-time agent streaming using Server-Sent Events (SSE) and WebSocket protocols, alongside RESTful and event-driven API architectures for agent orchestration.
• Integrate cloud-based LLM providers (AWS Bedrock, Azure OpenAI, Anthropic Claude, OpenAI GPT-4) and design prompt management systems with versioning, templating, and dynamic compilation.
• Implement conversation state persistence using Redis checkpointing and build tool-calling protocols (Model Context Protocol, function calling) for external data sources and APIs.
• Develop hybrid intelligence patterns combining LLM reasoning with rule-based logic and statistical analysis, and build response transformation pipelines for structured analytical outputs.
• Integrate observability platforms (Langfuse, LangSmith, or similar) to enable end-to-end agent tracing, telemetry, performance monitoring, and cost optimization across production workflows.
• Implement evaluation frameworks measuring agent success rates, reasoning quality, and output accuracy, while continuously optimizing token usage and LLM costs.
• Ensure enterprise security integration (LDAP, SSO, access control), robust error handling, and compliance with data governance and Responsible AI standards.
• Partner with data engineers, business analysts, and UX teams to translate requirements into scalable agent workflows and streaming interfaces.
• Mentor junior engineers on async Python patterns, agent design, and LLMOps best practices; participate in architecture reviews and contribute to documentation and knowledge sharing.

We hire for skills and capabilities, not just credentials - if this role excites you, but doesn t perfectly match your resume, we encourage you to apply anyway.

How We Work

Where you work matters - collaboration, innovation and patient impact happen in many settings. Our roles are structured across four work models: site-essential, site-by-design, field-based and remote-by-design. The model assigned to this role is based on its core responsibilities.