Head of ML & MLOps Engineering - Fintech Engineering

On-siteStaffMLOpsJust posted

✦Role summaryAI-generated

The role leads the design and implementation of a production‑grade ML platform that ensures model serving, monitoring, reproducibility and automated retraining for fintech applications. It also establishes responsible AI practices, including bias checks, explainability, and rigorous credit decisioning validation.

Skills required

MLOps Model serving Monitoring Feature store Explainability Bias detection Orchestration Reproducibility Credit scoring Champion challenger testing AI Data engineering Fraud detection Databricks Spark

About this role

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