> Markdown version of [/jobs/ext/3946906-ai-engineer](https://www.wearedevelopers.com/jobs/ext/3946906-ai-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # AI Engineer - **Company:** Harnham - **Location:** London, UK - **Salary:** £130,000.0 - £160,000.0 - **Contract:** Permanent contract - **Skills:** LangGraph Framework, AI Evaluation, Artificial Intelligence, Automated Storage and Retrieval Systems, Software as a Service, Cloud Computing, Database Development, Python (Programming Language), Performance Tuning, Search Technologies, Software Engineering, AI Infrastructure, Datadog, Retrieval-Augmented Generation, Large Language Models, Generative AI, Backend, Agentic-AI, Fastapi, Build Management, Machine Learning Operations - **Published:** October 27, 2026 - **Apply:** https://www.reed.co.uk/jobs/ai-engineer/57495986 ## About the Role * Strong commercial experience developing software and AI applications in Python. * Hands-on experience building and deploying LLM, generative AI, or machine learning systems in production. * Expertise with LangGraph, LangChain, or similar orchestration frameworks. * Experience designing and improving RAG architectures and vector database solutions. * Knowledge of AI evaluation, observability, and cloud-based software engineering best practices. ## Description This is an opportunity to join a high-calibre engineering team building production-grade AI systems that solve complex, real-world business challenges. You'll work on cutting-edge LLM applications, agentic workflows, retrieval systems, and AI infrastructure, with the chance to influence technical direction and contribute to a fast-growing product at the forefront of applied artificial intelligence., * Build and deploy agentic AI workflows using tools such as LangGraph and LangChain. * Design and optimise RAG pipelines, including vector search, embeddings, and retrieval strategies. * Develop LLM-powered applications for document intelligence, classification, extraction, and reasoning. * Create evaluation frameworks and observability tooling to monitor AI performance, quality, and cost. * Engineer scalable backend services using Python, FastAPI, cloud technologies, and modern software engineering practices.