Head of ML & MLOps Engineering - Fintech Engineering

Job Description

The mission
Build a state-of-the-art ML platform and the discipline around it.

• Models with a price tag. Every model has a business case and a measurable monetary outcome.
• Credit and decisioning models built with rigorous validation, champion/challenger testing and explainability.
• A production ML platform. Serving, monitoring, reproducibility and retraining are engineered, not improvised.
• Responsible AI, built in. Model risk, bias and explainability checks, with an independent sign-off gate before anything reaches production.
• Agent-first systems. Agents are production components with orchestration, guardrails, evals and observability.
• ◆ Models on governed data. You build on a point-in-time-correct feature store, not around it.

This is a business function. Every model carries monetary value, and you will run the function that way: compute budget, headcount and return on investment.

Qualifications

What you'll own

• The ML & MLOps team, from your first hire onward.
• Model-development standards and the validation methodology that stands up to model-risk and regulatory scrutiny.
• The ML platform behind decisioning services.
• A clear ownership line between feature production (data engineering) and model consumption, set together with the Head of Data Engineering and the Director.

You are

• A leader who loves data and loves building systems around it.
• Hands-on when needed, especially with AI on board. You understand the model, the pipeline and the serving layer.
• Experienced across the full ML lifecycle: development, validation, deployment, monitoring and retraining.
• Experienced in credit-scoring or underwriting modelling, or comparable high-stakes ML.
• Skilled in model-risk management and responsible-AI governance.
• Experienced in building and leading a team from zero.
• Fluent in English (B2+). [add years of experience: suggest 7+ years in ML, 3+ leading]

Bonus

• CCD2 and consumer-credit regulation · DORA/ICT risk · IFRS 9 implications for model outputs · fraud-detection ML · Databricks/Spark.

Additional Information

Why this one

• Seat at the table on a core leadership team.
• Build it right the first time. No legacy ML estate.
• Models that matter. Your work decides real money, not a dashboard.
• Real pace. A lean, AI-native organisation.