Python Engineer (Langgraph)

Firma apreel powstała w kwietniu 2010 roku. W miarę rozwoju firmy i równolegle ze wzrostem poziomu zaufania klientów, jej działalność poszerzyła się o usługi Outsourcingu Specjalistów IT. Dziś to właśnie ten obszar stanowi główny filar działalności apreel.

Must-have skills:

• 7+ years Python
• Deep LangGraph experience
• Strong LangChain ecosystem knowledge
• Production FastAPI — streaming responses, dependency injection, middleware, async patterns
• Celery + Redis broker in production
• Concurrency in Python — asyncio (gather, structured concurrency, cancellation), threading boundaries, mixing sync and async code safely
• Multi-datastore operations — MongoDB + Redis + Postgres
• OpenAI API at scale — rate limits, retries with exponential backoff, fallback model routing, streaming, tool/function calling
• Agent design patterns — ReAct, plan-and-execute, supervisor patterns, tool-use loops, multi-turn state, interrupt resumption
• Prompt engineering with discipline
• Token cost optimisation
• Production LLM observability
• Testing discipline — pytest (including pytest-asyncio)
• Pydantic v2 fluency, type-hinted code throughout

Nice to haves:

• RAG production experience (vector stores: Pinecone, Qdrant, pgvector)
• Production incident command for LLM-powered systems
• ML engineering background
• Anthropic / Claude API experience in addition to OpenAI
• Data pipeline experience (Airflow, Dagster, Prefect)
• Experience working with cross-team JSI / native bridges (the Python core integrates with a mobile JSI layer)

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-transfer sessions from the client's AI team

Ongoing:

• Set the technical direction for the AI core
• Lead design for new agent flows and major changes to existing ones
• Own the production health of the AI surface (with platform/SRE support)
• Hire and mentor the rest of the AI squad (~10 engineers at full scale)
• Represent the AI core in cross-team architecture conversations with the client