Artificial Intelligence Engineer

Hunter Bond; Greater London; Hybrid; GBP 72,000 - 88,000; Full time

Job summary

Hunter Bond is seeking an AI Engineer to help build the future of AI in financial services from London. This hybrid role involves turning ideas into live, governed AI systems with real budget and executive support.

You will design data pipelines, deploy AI solutions with supervision from the AI innovation team, and build agentic workflows while ensuring security and auditability across platforms.

Qualifications

• Degree in CS/DS/Engineering or equivalent hands-on experience.
• 1+ years in data engineering, AI engineering, analytics engineering, software engineering, BI or automation.
• Strong Python/SQL with experience building pipelines and datasets.
• Experience with Snowflake and Power BI is beneficial.
• Hands-on AI/ML experience, LLM APIs and governance awareness.

Responsibilities

• Designing and building data pipelines and curated datasets for AI and analytics.
• Partner with AI innovation to deploy real business AI solutions.
• Build generative AI, retrieval-augmented generation and agentic workflows.
• Create secure integrations across systems and data providers.
• Build dashboards to show business value and adoption.
• Assist with model governance, testing and monitoring.
• Rescue prototypes into owned, supported platforms.
• Set secure development and release practices for AI work.

Skills

Python

SQL

Data pipelines

APIs

ML experience

Education

Computer Science / Data Science or related

Maths / Quant Finance background

Tools

Snowflake

Power BI

GitHub / CI-CD

Job description

AI Engineer — Build the Future of AI in Financial Services

Location: London (hybrid)

Salary: Up to £80k + Bonus + benefits

Type: Permanent, full-time

The Opportunity :

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

What You'll Bring :

• 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.