AI Engineer

About the Role

Build and deploy production-grade AI agent systems on our enterprise GenAI platform. You'll own the full lifecycle — from agent design to cloud deployment — with a pragmatic bias toward simplicity and measurable ROI.

What You'll Do

• Build multi-agent systems using LangGraph, LangChain, Google ADK, and PyVegas following A2A and MCP protocols
• Expose AI logic as FastAPI services with async task handling and streaming
• Own deployments on Cloud Run — Dockerfiles, Cloud Build CI/CD, Cloud SQL, Secret Manager, canary rollouts
• Instrument agents with LangSmith — tracing, evaluation pipelines, prompt versioning, and CI-gated regression tests
• Apply context engineering discipline: RAG re-ranking, token budget management, structured tool responses
• Default to deterministic code; escalate to agents only when the problem genuinely requires reasoning

What You Bring

• 4+ years Python engineering with production deployments
• 2+ years with LangChain / LangGraph (v1.x) — stateful agents, graph compilation, checkpointing
• LangSmith — tracing, evaluations, prompt hub, CI integration
• FastAPI — async endpoints, middleware, background workers
• Docker + GCP — Cloud Run, Cloud Build, Artifact Registry, Cloud SQL, Secret Manager
• PostgreSQL for agent state, task queues, and LangGraph persistence

• PyVegas or equivalent enterprise LLM platform wrapper
• Google ADK, DSPy, LiteLLM, Vertex AI / Gemini
• A2A / MCP protocol experience
• New Relic or Galileo for GenAI observability

LangGraph LangChain LangSmith PyVegas Google ADK DSPy LiteLLM FastAPI Cloud Run Vertex AI PostgreSQL Docker MCP A2A

Qualifications

Masters

Range Of Year Experience-Min Year

8

Range Of Year Experience-Max Year

10