We’re looking for an experienced Python AI Engineer to build and productionise Generative AI and RAG solutions. You’ll work across AI orchestration, retrieval, evidence processing, LLM integration, response generation and validation, collaborating with engineering, data and platform teams.
Key Responsibilities
Build and maintain production-grade Python AI/RAG services.
Develop multi-step and agentic AI workflows using structured outputs, tool calling and schema validation.
Implement vector, lexical and hybrid retrieval, reranking, metadata filtering and evidence selection.
Build robust asynchronous services with appropriate retries, timeouts and failure handling.
Develop automated unit, integration, contract and regression tests.
Implement logging, tracing, monitoring and performance optimisation.
Improve AI quality, latency, cost, token usage and reliability through evaluation and production feedback.
Essential Experience
Strong commercial Python development experience, including async programming, typing, testing and performance optimisation.
Proven delivery of Generative AI and RAG solutions into production.
Hands‑on experience with Pydantic, LangGraph, Semantic Kernel / Microsoft Agent Framework or similar.
Strong understanding of LLM integration, structured outputs, tool calling, prompt/model management and fallback strategies.
Strong knowledge of embeddings, vector/lexical search, hybrid retrieval, query decomposition, chunking and reranking.
Experience with Pytest, APIs, Git, CI/CD, Docker and observability.
Strong troubleshooting skills across application, retrieval, model, concurrency and performance issues.
Desirable
Azure OpenAI / Azure AI Search / Azure AI Foundry
Pydantic AI, LangGraph or Semantic Kernel
RAG/LLM evaluation and retrieval-quality metrics
Kubernetes, telemetry and load testing
Knowledge graphs, temporal retrieval or document linkage
AI monitoring, governance and prompt-injection protection
Experience working in regulated or specialist knowledge domains
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