Head of ML & MLOps Engineering - Fintech Engineering

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

About InPost

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

Responsibilities

• 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.
• What you'll own The agentic standard for ML.
• You run agentic model iteration and agentic model monitoring and observability, bound to the feature store.
• You own the reliability, cost and latency of agent loops and graphs in production, and you hire against the bar.
• 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.
• You are A leader who loves data and loves building systems around it.
• You understand the model, the pipeline and the serving layer.

Requirements

• Hands-on when needed , especially with AI on board.
• development, validation, deployment, monitoring and retraining.
• Experienced in architecting agentic systems for ML: orchestration, guardrails, evals in CI, human-in-the-loop checkpoints, agent observability.
• Experienced in building and leading ML teams in regulated settings.
• Skilled in model-risk management and responsible-AI governance.
• Experienced in building and leading a team from zero.
• Fluent in English (B2+).

Preferred

• Experienced in credit-scoring or underwriting modelling, or comparable high-stakes ML.
• CCD2 and consumer-credit regulation · DORA/ICT risk · IFRS 9 implications for model outputs · fraud-detection ML · Databricks/Spark.
• What you will work with The ML platform (serving, monitoring, and retraining), the feature store, the agent evaluation and observability layer, and cross-functional partners in Data Engineering and Credit Risk.
• 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.