Principal AI Engineer
bms
Job Description
- 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.