Ai Python Developer Lead

Principal Python + LangGraph Engineer Engagement: Atlas (US health-tech, AI-driven mobile coaching platform) Engagement context We are partnering with a US-based health-tech company on the takeover of a production AI-powered mobile coaching platform. The platform is built around a Python AI core (atlas-ai) which runs:

- A FastAPI chat surface with intent routing and tool-using agents

- A LangGraph-based agent framework with multi-agent orchestration

- A Celery + Redis task queue for asynchronous agent flows

- MongoDB for fitness-plan storage, Redis for conversation state, Postgres for LangGraph checkpoints

- Several agent personas: onboarding, chat, plan creation, in-workout smart adjust, plan smart adjust, habit formation

- Direct OpenAI integration for the LLM layer This is the product. If the AI core breaks, the product breaks. Role summary As Principal AI Engineer you are the technical owner of the AI core. You lead the audit of the existing codebase, define the architecture we evolve toward, build the test and evaluation harness that lets us ship changes safely, and you are the engineer who is paged when the AI surface misbehaves in production. You will be the deciding voice on whether we can responsibly take this service in our scope or whether we keep the client's original team embedded longer. What you'll do First 90 days

- Audit atlas-ai: agent flows, LangGraph state machines, Celery topology, datastore usage, OpenAI integration patterns

- Produce a written assessment of operational risk: failure modes, race conditions, retry semantics, idempotency, checkpoint integrity

- Quantify token cost per agent flow and per user session

- Identify the highest-risk subsystems and propose stabilisation plans

- Build (or harden) an evaluation harness for the agent flows — golden cases, regression suites, hallucination/safety tests

- Lead the knowledge-