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# AI Engineer

- **Company:** Anaplan
- **Location:** London, UK
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Microsoft Azure, Relational Databases, Software Debugging, Python (Programming Language), MySQL, Google Cloud, Cloud Platform System, ReactJS, Large Language Models, Multi-Agent Systems, Prompt Engineering, Generative AI, Kubernetes, Machine Learning Operations, Virtual Agents, Docker
- **Published:** June 11, 2026
- **Apply:** https://uk.indeed.com/viewjob?jk=5476ec8b530a06f2

## About the Role

Do you have experience in Relational databases?, * Strong proficiency and experience working with Python-based services

 * Hands-on experience with relational databases (MySQL or similar).
 * Familiarity with React and ability to support front-end AI integrations.
 * Basic understanding of large language models and eagerness to expand knowledge.
 * Demonstrated interest or experience in prompt engineering and working with LLMs.
 * Strong problem-solving, debugging, and collaboration skills.

Desirable

 * Prior experience with vector databases (Pinecone, Weaviate, FAISS, etc.).
 * Knowledge of retrieval-augmented generation (RAG) pipelines.
 * Experience with AI/ML infrastructure or agent frameworks.
 * Familiarity with cloud environments (AWS, GCP, Azure) and containerised deployments (Docker, Kubernetes).

## Description

* Design, build, and maintain services and infrastructure that manage AI-powered agents.
 * Develop backend services in Python, with comprehensively tested and self-documented APIs.
 * Work with Agentic AI frameworks and implement prompt engineering best practices to build scalable foundational platform.
 * Work with relational databases (MySQL) to store, query, and optimise structured data.
 * Collaborate with product and research teams to refine and implement the product roadmap.
 * Support front-end teams using React/JS components and wrappers for AI-driven experiences.
 * Learn, apply, and optimise RAG systems, including vector databases, embeddings, and document chunking strategies.
 * Build a practical understanding of how large language models (LLMs) work and deepen expertise in effective interactions with LLMs for agent orchestration.
 * Monitor, debug, and optimise system performance, ensuring high reliability in production environments.


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