Senior AI Engineer; Investment Management

I’m working with a highly sophisticated, data-driven investment organisation that is building out its AI engineering capability within the Investment function.

This is an opportunity for a senior AI Engineer to work directly alongside quantitative researchers and portfolio managers
, turning real investment workflows into production-grade AI systems.

This is not a research-only AI role and it isn't simply about experimenting with the latest LLMs. The focus is on building technology that is genuinely useful to investment professionals: agentic workflows, reusable AI skills and automation that can safely accelerate research and portfolio processes.

You will sit close to the Investment teams, working with domain experts to identify opportunities where AI can meaningfully improve productivity and decision support.

You'll be responsible for taking often ambiguous investment requirements and turning them into well-defined, production-ready software.

This includes:

• Designing, building and operating LLM-powered and agentic workflows for investment use cases
• Developing reusable AI skills that encode investment workflows and institutional knowledge
• Working directly with quantitative researchers and portfolio managers to prototype and product ionise solutions
• Building robust Python and AWS services and integrating them into existing production environments
• Establishing appropriate human-in-the-loop review, testing and controlled execution
• Designing guardrails around data access, agent scope, cost and reliability
• Translating investment intent into clear technical specifications and well-scoped engineering work
• Measuring the real-world impact of AI workflows through adoption, quality and time-to-value
• Establishing sensible review, auditability and accountability standards around AI-generated changes
• Helping shape best practices around agentic and instruction-driven development as the technology evolves

We're particularly interested in engineers who combine strong software engineering fundamentals with genuine interest or experience in investment environments
.

You should have:

• Significant experience delivering production-grade software or AI systems
• Hands-on experience building and operating LLM, GenAI or agentic systems
• Strong Python and AWS experience
• Strong engineering practices across testing, Git/version control and CI/CD
• Experience taking complex or ambiguous business/domain requirements and turning them into reliable software
• Experience working closely with sophisticated technical or domain experts
• An understanding of systematic, quantitative or institutional investment processes
• Good judgement around the trade-offs between quality, speed, cost and risk
• A pragmatic mindset: you care about whether an AI system actually works and creates value, not simply whether the technology is impressive

A background in quantitative finance, systematic investing, hedge funds, asset management or financial technology would be particularly valuable.

The strongest candidates are likely to be people who can comfortably sit between AI engineering and investment
.

You might currently be a:

• Senior / Staff Software Engineer working heavily with GenAI
• Quant Developer with substantial recent AI/LLM experience
• Engineer embedded within a quantitative research or investment team

The common denominator is hands-on production engineering combined with the ability to understand a complex domain and build technology around it.

This would suit someone who wants to move beyond generic enterprise AI and work on AI that directly interacts with the investment decision-making process
.

If you're an AI Engineer who wants to work at the intersection of agentic AI, software engineering and quantitative investing
, I'd be very interested in speaking with you.