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Head of ML & MLOps Engineering - Fintech Engineering
Contract type
Ongoing
Work mode
Remote · Poland
Experience
Lead / principal · Not stated
Level inferred from job title
Job description
Lead the creation of a new ML and MLOps function inside InPost, building a production ML platform, credit and decisioning models, and agentic systems with responsible-AI governance. The role owns the ML team, model-development standards, and the monetary impact of every model.
At a glance
- Work mode
- Remote · Poland
- Languages
- English – B2+
What you'll do
- Own the agentic standard for ML: run agentic model iteration and agentic model monitoring and observability, bound to the feature store
- Own the reliability, cost and latency of agent loops and graphs in production, and hire against the bar
- Build the ML & MLOps team, from the first hire onward
- Own model-development standards and the validation methodology that stands up to model-risk and regulatory scrutiny
- Own the ML platform behind decisioning services
- Set a clear ownership line between feature production (data engineering) and model consumption, together with the Head of Data Engineering and the Director
- Build credit and decisioning models with rigorous validation, champion/challenger testing and explainability
- Engineer serving, monitoring, reproducibility and retraining for the production ML platform
- Build in responsible AI: model risk, bias and explainability checks, with an independent sign-off gate before anything reaches production
- Build agent-first systems as production components with orchestration, guardrails, evals and observability
- Build on a point-in-time-correct feature store
- Run the function as a business function: compute budget, headcount and return on investment
What we're looking for
- A leader who loves data and loves building systems around it
- Hands-on when needed, especially with AI on board; understands the model, the pipeline and the serving layer
- Experienced across the full ML lifecycle: 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
- 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+)
Nice to have
- CCD2 and consumer-credit regulation
- DORA/ICT risk
- IFRS 9 implications for model outputs
- Fraud-detection ML
- Databricks/Spark
Your team
Data & AI
A scale-up inside InPost where AI flows through the business; you would join the core Data & AI leadership team.
Benefits
- 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
Apply
Direct apply at InPost
Opens the job posting at InPost
About InPost
Building a scale-up inside InPost, with AI flowing through it.
Key facts
- Building a scale-up inside InPost
- Core Data & AI leadership team backed by the InPost executive team
- Extreme ownership: you own a model from first idea to its profit and loss impact
What they value
- Extreme ownership