Head of ML & MLOps Engineering - Fintech Engineering

InPost

About The Position

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. The mission is to 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 where serving, monitoring, reproducibility and retraining are engineered, not improvised. Responsible AI, built in, with model risk, bias and explainability checks, and an independent sign-off gate before anything reaches production. Agent-first systems where agents are production components with orchestration, guardrails, evals and observability. Models on governed data, built on a point-in-time-correct feature store. 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.

Requirements

Leadership experience with a passion for data and building systems around it.
Hands-on capability, especially with AI.
Understanding of the model, the pipeline, and the serving layer.
Experience across the full ML lifecycle: development, validation, deployment, monitoring, and retraining.
Experience in credit-scoring or underwriting modeling, or comparable high-stakes ML.
Skilled in model-risk management and responsible-AI governance.
Experience in building and leading a team from zero.
Fluent in English (B2+).
7+ years in ML.
3+ years in leadership.

Nice To Haves

CCD2 and consumer-credit regulation
DORA/ICT risk
IFRS 9 implications for model outputs
Fraud-detection ML
Databricks/Spark

Responsibilities

Own the ML & MLOps team, from your first hire onward.
Define and implement model-development standards and the validation methodology that stands up to model-risk and regulatory scrutiny.
Oversee the ML platform behind decisioning services.
Establish a clear ownership line between feature production (data engineering) and model consumption, in collaboration with the Head of Data Engineering and the Director.
Build and lead a team from zero.