Head of ML & MLOps Engineering - Fintech Engineering

* 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.