LLM Engineer / AI Engineer

My client is a fast-growing AI technology company building intelligent systems deployed in real-world, safety-critical environments. Their solutions combine advanced AI, data, and edge technologies to support decision-making and reduce risk in high-hazard industrial settings. They are now looking to hire a LLM Engineer to help design and deliver large language model-powered capabilities across internal platforms and customer-facing products. This is a hands-on, production-focused role for someone with strong real-world LLM experience, not a purely research or experimental background. The Role You will:

• Design, build and deploy LLM-driven features into live products and platforms
• Work with both commercial and open-source LLMs, selecting the right model for each use case
• Build and optimise RAG pipelines, embeddings and vector-based retrieval solutions
• Develop APIs and services that integrate LLMs with existing AI, data and platform systems
• Optimise solutions for performance, reliability, latency and cost
• Collaborate with engineering, AI and product teams to identify and deliver high-value use cases
• Ensure all solutions meet security, compliance and data governance standards

• Proven experience deploying LLMs in production
• Strong Python development skills
• Hands-on experience with:
• Prompt engineering and evaluation
• Retrieval-Augmented Generation (RAG)
• Embeddings and vector databases (e.g. FAISS, Pinecone, Weaviate, Chroma)
• Experience building LLM-backed services using FastAPI / Flask or similar
• Strong understanding of trade-offs around accuracy, latency, cost and scalability

Highly Desirable

• Experience working with or fine-tuning open-source LLMs
• Familiarity with LLMOps (monitoring, evaluation, guardrails, versioning)
• Experience integrating LLMs into complex or data-heavy systems
• Docker and Linux experience
• Background in regulated, industrial or safety-critical environments

The Right Mindset

• Pragmatic, delivery-focused and comfortable working with ambiguity
• Able to translate complex AI concepts into practical solutions
• Confident owning problems end-to-end, from idea through to deployment
• Motivated by building AI that has real-world impact