AI Platform Engineer

Oliver Bernard; Greater London; On-site; GBP 150,000 - 200,000; Full time

Job summary

Oliver Bernard is recruiting an AI Platform Engineer to own the platform that makes AI work in production. You will handle agent runtime infrastructure, model tooling, and the APIs connecting data, models and user interfaces from a production perspective.

This role sits between research and DevOps, requires hands-on experience with LLM APIs, Python backend engineering, and cloud deployment. Central London, four days a week in the office, with opportunities to shape investment research through

Qualifications

• PhD required or strongly preferred in CS/ML/physics/math or similar.
• Strong Python backend engineering experience.
• Hands-on LLM API and model endpoint integration.
• Cloud deployment, observability and production reliability.
• Frontend ability to build usable interfaces for internal tools.

Responsibilities

• Own agent runtime infrastructure and model/tool orchestration.
• Own APIs between data, models and user interfaces.
• Own auth, permissions, logging, monitoring and audit trails.
• Own usage, cost and error tracking across LLM workflows.
• Develop internal tools for market dailies, consensus summaries and sales-ready content.

Skills

Python backend

LLM API

Cloud deployment

Observability

Education

PhD (CS, ML, Physics, Maths or similar)

Tools

FastAPI

Model endpoints

Job description

AI Platform Engineer - £150-200k - 4 days a week in the office

PhD + production engineer? This one’s for you.

I’m partnering with a leading independent global macro research firm, part of a major international financial group. They’re hiring an AI Platform Engineer to turn their AI agents, quant models and data into products that institutional investors use every day.

This isn’t a research role, and it isn’t a pure DevOps role either. It sits between the two: you’ll own tdhe platform that makes AI work in production.

What you’ll own

• Agent runtime infrastructure and model/tool orchestration
• The APIs between data, models and user interfaces
• Auth, permissions, logging, monitoring and audit trails
• Usage, cost and error tracking across LLM workflows
• Internal tools that produce market dailies, consensus summaries and sales-ready content

What they’re looking for

• A PhD (CS, ML, Physics, Maths or similar), ideally followed by time in industry
• Strong Python backend engineering, with FastAPI or similar API design
• Hands-on LLM API and model endpoint integration
• Cloud deployment, observability and production reliability
• Enough front-end ability to put a usable interface on internal tools
• Financial markets experience is a real plus, especially working alongside quants or front-office teams.

Central London, 4 days a week in the office

If you’ve shipped LLM systems to real users and want your work to directly shape investment research, this one is for you.