Python AI Developer

Britenet; Warszawa; On-site; PLN 240,000 - 360,000; Full time

Job summary

Britenet is seeking an experienced Python developer to design and build API-enabled LLM-powered services in Warsaw. You will work on cutting-edge GenAI/LLM engineering, building scalable pipelines and responsive systems for clients.

You will implement RAG pipelines, tool-calling patterns, and multi-step reasoning workflows, with emphasis on quality engineering, observability, and clear communication with stakeholders across markets.

Qualifications

• 5+ years commercial Python experience, incl 3+ years hands-on GenAI/LLM engineering.
• Strong fundamentals for scalable services/APIs using FastAPI/Flask/Django.
• Hands-on experience building RAG pipelines: retrieval, embeddings, vector search.
• Working knowledge of agentic patterns: tool-calling, function-calling, multi-step reasoning workflows.
• Strong prompt engineering skills with structured outputs (JSON schemas, Pydantic, Instructor or equivalent).
• Experience with AWS/Azure/GCP and managed AI/ML services (Bedrock, Azure OpenAI).
• An evals mindset – you think about relevance, latency, and cost as real engineering concerns.
• High-proficiency written and spoken English – daily use with clients and teammates.

Responsibilities

• Design and build Python services and APIs that wrap LLM-powered functionality.
• Build and maintain agentic and RAG pipelines: retrieval, reranking, tool-calling, multi-step reasoning, structured outputs.
• Care about quality beyond "it works" – evals, observability, and the data that tells you when something regresses.
• Work directly with clients: translate fuzzy business requirements into architecture decisions, and explain your technical tradeoffs to non-technical stakeholders.
• Work across a distributed, multi-market team and client organizations.

Skills

Python development

GenAI/LLM engineering

API design & development

Prompt engineering

Evaluations mindset

English communication

Tools

FastAPI/Flask/Django

pytest

RAG pipelines

AWS/Azure/GCP

Bedrock / Azure OpenAI

Vector search

Job description

Qualifications

• 5+ years commercial Python experience, including 3+ years of hands‑on GenAI/LLM engineering.
• Solid engineering fundamentals: building scalable services/APIs (FastAPI, Flask, or Django), writing real test suites (pytest), clean and modular architecture.
• Hands‑on experience building RAG pipelines – retrieval, embeddings, vector search.
• Working knowledge of agentic patterns: tool‑calling, function‑calling, multi‑step reasoning workflows.
• Strong prompt engineering skills, including structured outputs (JSON schemas, Pydantic, Instructor or equivalent).
• Experience with at least one major cloud platform (AWS, Azure, or GCP), including its managed AI/ML services (e.g., Bedrock, Azure OpenAI).
• An "evals mindset" – you think about relevance, consistency, latency, and cost as real engineering concerns, not afterthoughts.
• High‑proficiency written and spoken English – you'll use it daily with clients and teammates.

Nice to have

• Experience with orchestration frameworks beyond the basics – LangGraph, LangSmith, LlamaIndex.
• Hands‑on with a specific vector database (Pinecone, Weaviate, Milvus, pgvector) beyond "I integrated one once."
• Experience building evaluation frameworks or golden‑dataset pipelines specifically (as opposed to just using one).
• Exposure to data pipeline work feeding AI systems – understanding how data quality/freshness affects model behavior.
• Prior client‑facing / consulting experience in a professional‑services or consulting setup.

Main responsibilities

• Design and build Python services and APIs that wrap LLM‑powered functionality.
• Build and maintain agentic and RAG pipelines: retrieval, reranking, tool‑calling, multi‑step reasoning, structured outputs.
• Care about quality beyond "it works" – evals, observability, and the data that tells you when something regresses.
• Work directly with clients: translate fuzzy business requirements into architecture decisions, and explain your technical tradeoffs to non‑technical stakeholders.
• Work across a distributed, multi‑market team and client organizations.