Head of ML & MLOps Engineering - Fintech Engineering

Employment: Contract Experience level: Director 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. Company Description Build ML that makes money and stands up to a regulator. We are building a scale-up inside InPost, and AI will flow through it. You would join the core Data & AI leadership team and build the ML and MLOps function from zero, with the InPost executive team backing it. Our word is extreme ownership : you own a model from first idea to its profit and loss impact.