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. 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. 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. 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. Originally posted on Himalayas