Popis nabídky

Principal Python + LangGraph Engineer @ apreel Sp. z o.o. v Česko

apreel Sp. z o.o.

Na dálkuPráce odkudkoli
SmlouvaNa dobu určitou nebo freelance

AI / ML

180 zł PLN / hour

Česko

Před 10 dny

Odpovědnosti

Původní popisek.

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

Ukázat vše (11)

O pozici / o projektu

Původní popisek.

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. If LLM token cost runs away, our margin runs away. If the agent flows behave unpredictably under load, real users get bad coaching.

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. The audit conclusions you write in the first 4 weeks will inform the contractual scope of the entire engagement.

Offer:

Location: 100% remote
Engagement: B2B
Rate: upt to PLN 180 p/h
Start: asap

Detail pracovní nabídky

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Odpovědnosti

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

O pozici / o projektu

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. If LLM token cost runs away, our margin runs away. If the agent flows behave unpredictably under load, real users get bad coaching.

Role summary

Offer:

Location: 100% remote

Engagement: B2B

Rate: upt to PLN 180 p/h

Start: asap