Applied AI Engineer (LLMs / Agents / RAG)

AI1 AI & Machine Learning Greater London £130K - £160K / Year

London – On-site - £160,000+ and Equity

I’m partnering with a high-growth, venture-backed AI platform that’s transforming how global financial institutions automate and scale complex client work.

The business has achieved exceptional traction in under a year, is backed by top-tier global investors, and is now expanding its applied AI team to build production-grade agent systems at scale.

The role

This is a hands-on Applied AI Engineer position with real ownership. You’ll lead and build significant parts of the AI agent infrastructure — from multi-agent orchestration and RAG pipelines through to evaluation frameworks and production deployment.

You’ll work directly with the founders, alongside a small, elite engineering team, delivering AI-powered features used by enterprise clients in live environments.

What you’ll be doing

• Designing, building, and deploying AI agent systems in production
• Implementing Retrieval-Augmented Generation (RAG) pipelines
• Developing evaluation frameworks to measure model quality, reliability, and safety
• Building scalable backend services and APIs (Python preferred)
• Creating data pipelines for model training, evaluation, and continuous improvement
• Ensuring performance, scalability, and security across AI systems
• Contributing across the stack — from backend services to deployment pipelines
• Mentoring junior engineers and promoting strong engineering standards

What they’re looking for

• 5+ years professional software engineering experience
• Proven experience deploying AI applications in production
• Strong backend engineering skills (Python / Django / FastAPI ideal)
• Hands-on experience with RAG, AI agents, or LLM orchestration
• Familiarity with LLM evaluation techniques and performance monitoring
• Solid understanding of relational databases
• Experience with cloud platforms, Docker, Kubernetes, and CI/CD
• Background workers and task queues (Celery, RQ)
• Experience using Redis for caching or job queues
• Comfortable operating in fast-moving, ambiguous environments

Why this role stands out

• Direct exposure to founders with multiple successful exits
• Competitive salary + meaningful equity
• Real ownership of AI systems used by enterprise clients
• The opportunity to help build a category-defining AI company

What to expect

This is not a slow or easy role. The pace is high, priorities shift, and the problems are genuinely hard.

But if you’re motivated by impact, ownership, and building real AI systems in production — this is a rare opportunity to make a defining career move.

If this sounds like you, apply or message me directly for a confidential conversation.