AI Consultant – Financial Services & Risk

Build GenAI that has to work in production, survive an audit, and satisfy a regulator.

Your role:

At Zanders, GenAI is not an R&D experiment or an innovation lab. Our GenAI team — currently seven to ten consultants — delivers at enterprise scale for banks, insurers and financial institutions across Europe. Recent work includes autonomous agents for financial crime automation, and enterprise GenAI coding tools that accelerate risk modelling workflows. Both were built from scratch, both run in production, and both had to hold up under regulatory scrutiny.

Much of this work has no established playbook yet. That is the appeal and the difficulty. You will often be the first person at Zanders to tackle a particular problem — backed by a team who have done the equivalent before, and by thirty years of the firm's standing in financial risk.

The team comes from mixed backgrounds: quantitative research, software engineering, model validation and consulting. What holds it together is people who have actually shipped LLM systems and can explain them to a Chief Risk Officer.

Projects range from focused sprints of a few months to embedded engagements of one to two years. Demand is currently strongest in Financial Economic Crime — AML, fraud detection and transaction monitoring — but the role is not limited to that domain, and the portfolio is widening across risk disciplines.

We are hiring at both Senior Consultant and Manager level. The work is the same; the scope of ownership differs.

You have built LLM systems that other people depend on, and you want to keep doing it somewhere the constraints are real.

Key Responsibilities:

• Build. Design and implement GenAI solutions for clients — agent frameworks, RAG architectures, fine-tuned models and LLM tooling that has to hold up in production.

• Govern. Help clients manage the risks of the AI systems they are building or buying: writing AI risk policy, validating LLM-based systems, and translating the AI Act, Wwft and model risk requirements into practical guidance.

• Advise. Shape client strategy on where and how to deploy GenAI — assessing model landscapes, defining implementation roadmaps, and recommending architectures that are production-ready and defensible to a regulator.

• Take ownership of your workstream: plan the work, keep quality and timelines on track, and raise risks early.

• Balance concurrent engagements, planning ahead and adjusting when client priorities shift.

• Work directly with client stakeholders — explaining technical decisions to the people who will be accountable for them, and building trust as the work develops.

• Build relationships with the client teams you work alongside, and notice where Zanders could help them further.

• Help define the discipline publicly: write, present and speak. We want the team's thinking in the market, not only in client deliverables.

• Share what you work out internally. This is a young practice, and what you figure out becomes how we do it.

• Contribute to internal initiatives and to a team culture people want to be part of.

• At Manager level: lead engagements end to end, own the client relationship, and take accountability for commercial outcomes including origination.

• At Manager level: lead the consultants on your projects — set clear direction, delegate, support their development, and safeguard a healthy workload across the team.

Skills to be successful

Essential

• Demonstrated hands-on delivery of LLM systems — fine-tuning, RAG architectures, agent frameworks, prompt engineering or LLM evaluation. We assess this on what you have built and shipped, not on how long you have been doing it.

• Strong programming skills in Python.

• Experience with cloud AI/ML infrastructure, particularly Azure and Databricks.

• The ability to explain technical decisions clearly to non-technical stakeholders who will be accountable for them.

• Experience in financial services or another regulated industry. Our work sits at the intersection of AI and regulation, and that context matters from day one.

• Fluent English.

• Senior Consultant level: three to six years of professional experience, including a track record in quantitative or AI/ML delivery, ideally in a client-facing setting.

• Manager level: six to nine years, including leading quantitative or AI projects end to end with ownership of the client relationship, and the ability to contribute to business development.

Also valuable

• A Master's or PhD in a quantitative field such as mathematics, physics, computer science, econometrics or engineering. Valuable, but not required — we care more about what you have built than where you studied.

• Familiarity with the AI Act, Wwft or model risk management frameworks.

• Experience in financial crime — AML, fraud detection or transaction monitoring.

• A point of view on how AI should change financial services, and the confidence to argue it with senior stakeholders.

• Proficiency in both spoken and written Dutch. Our working language is English, so this is genuinely optional.