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# Python AI Engineer

- **Company:** Stott and May
- **Location:** London, UK
- **Contract:** Temporary contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Microsoft Azure, Computer Programming, Continuous Integration, Graph Database, Information Retrieval, Python (Programming Language), Load Testing, Performance Tuning, Regression Testing, Enterprise Search, Data Logging, Retrieval-Augmented Generation, Large Language Models, Generative AI, Git, Pytest, Kubernetes, Docker
- **Published:** September 30, 2026
- **Apply:** https://www.totaljobs.com/job/python-ai-engineer/stott-may-professional-search-limited-job108060850

## About the Role

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

## Description

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.


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