> Markdown version of [/jobs/ext/4035142-ai-full-stack-engineer-contract](https://www.wearedevelopers.com/jobs/ext/4035142-ai-full-stack-engineer-contract). 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 Full Stack Engineer (Contract) - **Company:** Harnham - **Location:** London, UK (Remote available) - **Salary:** £130,000.0 - £143,000.0 - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Applications Architecture, Microsoft Azure, Databases, Data Stores, Graph Database, Python (Programming Language), NoSQL, Search Technologies, Software Engineering, SQL Databases, Systems Integration, TypeScript, Cloud Platform System, Retrieval-Augmented Generation, Large Language Models, Backend, Agentic-AI, Front End Software Development, Docker - **Published:** October 2, 2026 - **Apply:** https://www.reed.co.uk/jobs/ai-full-stack-engineer-contract/57414111 ## About the Role * Strong experience as a Full Stack AI Engineer or in a similar AI-focused software engineering role. * Advanced capability with Python and TypeScript across frontend and backend development. * Hands-on experience with LLMs, RAG architectures, agentic AI, and modern AI frameworks. * Strong knowledge of APIs, system integration, application architecture, and database technologies. * Experience using Docker and deploying AI solutions within cloud environments such as Azure or GCP. * Knowledge of vector databases, semantic search, and knowledge graph technologies. * Experience deploying AI systems within secure or restricted environments is highly advantageous. * Exposure to defence, government, national security, or similarly regulated sectors is beneficial. * Active or previously held security clearance is advantageous. ## Description * Design and develop full stack AI applications using Python and TypeScript. * Build and enhance frontend interfaces, backend services, APIs, and application architectures. * Develop and deploy LLM-powered solutions, including RAG pipelines and agentic AI applications. * Integrate and manage SQL and NoSQL data stores, ensuring effective data modelling and system performance. * Implement vector databases, semantic search capabilities, and knowledge graph solutions. * Take AI products from proof of concept through to production-ready deployment, including secure infrastructure and containerised environments.