Artificial Intelligence Engineer

We’re hiring on behalf of a well-established, highly regarded name in financial services - one that’s investing seriously in AI right now, not just talking about it. Think real budget, real executive buy-in, and a genuine mandate to move fast and do it properly.

This is a rare seat: you’ll be one of the engineers actually building the AI capability from the ground up, working hand-in-hand with a newly formed AI innovation function to take ideas from “wouldn’t it be cool if…” all the way to live, governed, production systems trusted across the business.

If you’re the kind of engineer who gets excited by RAG pipelines, agentic workflows and LLM-powered automation - but also takes pride in shipping things that are secure, auditable and genuinely built to last - this is for you.

What You’ll Actually Be Doing:

• Designing and building data pipelines and curated datasets that power AI, analytics and automation across the business
• Partnering directly with stakeholders and the AI innovation team to turn real business problems into deployed AI solutions - not proof-of-concepts that die in a drawer
• Building out generative AI, retrieval-augmented generation, predictive analytics and agentic AI workflows
• Creating secure integrations across internal systems, data providers, document stores and AI platforms
• Building dashboards and MI (Power BI or similar) to show the business value and adoption of what you’ve built
• Helping stand up model governance - documentation, testing, monitoring, bias/fairness checks - so AI here is trusted, not just tolerated
• Rescuing promising prototypes from “shadow IT” limbo and turning them into properly owned, supported platforms
• Setting the standard on secure development, source control, testing and release practices for AI work

• A degree in Computer Science, Data Engineering, Data Science, Software Engineering, Maths, Quant Finance or similar - or equivalent hands-on experience
• 1+ years in data engineering, AI engineering, analytics engineering, software engineering, BI or automation
• Strong Python and SQL, with real experience building pipelines, APIs and analytical datasets
• Experience with Snowflake or a comparable modern data platform
• Practical Power BI / DAX / Power Query chops
• Genuine hands-on AI/ML experience - Python ML libraries, notebooks, prompt engineering, LLM APIs, RAG or agentic frameworks
• An understanding of the full AI lifecycle: discovery, data prep, prototyping, deployment, monitoring, handover
• Comfort working in - or strong interest in - a regulated environment where governance, security and audit trails matter
• GitHub/CI-CD literacy and solid engineering hygiene
• The communication skills to translate between business stakeholders, techies and control functions

Bonus points if you’ve worked in asset management, investment banking, banking ops, risk or compliance - but it’s not a dealbreaker if you haven’t.