Head of ML & MLOps Engineering Fintech Engineering

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 youll 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.

Remote Work :

Yes

Employment Type :

Contract