AI Engineer – LLMs, Azure & MLOps

About the role

Join a global professional services firm that is heavily investing in AI. You will design, build and deploy production‑ready large‑language‑model (LLM) solutions on the Microsoft Azure ecosystem, focusing on document‑centric business challenges.

Key responsibilities

• Design, build and deploy LLM and machine‑learning solutions using Azure Databricks, Azure Machine Learning and Azure AI Foundry.
• Develop scalable AI applications with modern orchestration frameworks such as LangChain and LangGraph.
• Create agentic AI workflows and enterprise services using retrieval, tool‑calling and orchestration patterns.
• Deliver end‑to‑end AI/ML pipelines covering experimentation, deployment, monitoring and optimisation.
• Apply MLOps and LLMOps best practices for model lifecycle management, governance and production monitoring.
• Collaborate with data engineers, platform engineers and business stakeholders to deliver enterprise AI solutions.
• Contribute to AI products in document intelligence, knowledge search, workflow automation and AI assistants.
• Help shape engineering standards, responsible AI practices and cloud governance.

Required profile

• Commercial experience building and deploying LLM‑powered applications into production.
• Strong hands‑on experience with Azure Databricks, Azure Machine Learning and preferably Azure AI Foundry.
• Good understanding of MLOps and LLMOps principles.
• Excellent Python engineering skills.
• Experience with Retrieval‑Augmented Generation, vector search, embeddings, prompt engineering and enterprise retrieval architectures.
• Background in document‑heavy sectors such as legal, financial services or insurance is a plus.
• Proficiency with Docker, Kubernetes, MLflow, CI/CD pipelines and Git‑based development.
• Strong communication skills for cross‑functional collaboration.

Required skills

• Python
• Azure Databricks
• Azure Machine Learning
• Azure AI Foundry
• LangChain
• LangGraph
• PyTorch
• Pydantic
• RAG (Retrieval‑Augmented Generation)
• Vector search & embeddings
• Prompt engineering
• Docker
• Kubernetes
• MLflow
• CI/CD
• Git

What we offer

• Hybrid working model (3 days office, 2 days remote).
• Salary up to £85,000 per year plus benefits.
• Opportunity to work on cutting‑edge AI products for enterprise clients.