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AI Engineer / LLM Engineer - London - Applied AI

London - 2-3 days hybrid

Permanent

£50,000 - £80,000

Consultant

Greg Clark
LLM Engineer | Pharma R&D | Central London | £70-80k

Applied AI engineering role with a large consultancy working on projects for a global pharmaceutical company automating R&D workflows using LLMs.

The Role
You'll be building production-grade AI systems that analyse clinical trial data and accelerate pharma research workflows. This is engineering work using foundation models from OpenAI and Anthropic - not research or model development.

You'll work within a 15-20 person AI team, owning specific features within larger AI systems. The work is fast-paced and focused on solutions that actually get deployed and used.

What You Need
Production experience is critical. We're looking for people who've actually shipped LLM-based systems to users, not just built proofs of concept.

Technical requirements:
• 1-2 years building LLM solutions deployed to production
• Strong Python coding skills
• Experience with LLM orchestration frameworks (LangChain, LangGraph, Crew AI, or similar)
• Knowledge of cloud infrastructure
• Understanding of how to build reliable systems that handle non-deterministic outputs
• Experience working in agile teams within larger organisations
• Good communication and task management skills

They are looking for AI Engineers / LLM Engineers who have built production AI systems and demonstrate this through your projects or work experience.

The Package
• Salary: £50,000 - £80,000
• Location: Central London (near St Paul's)
• Hybrid: 2-3 days per week in office
• Start date: Early February

The Environment
You'll be part of a 10,000+ person multinational organisation with a focused AI team. The role requires understanding how to work collaboratively on features within a bigger system, following good software development practices while thinking about resilience and reliability.

Interview Process
Expect 2-3 rounds covering your experience, technical ability through case studies, and team fit.

If you've deployed LLM systems to production and know how to build reliable AI solutions, we'd like to hear from you.
 
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