Head of ML & MLOps Engineering (Fintech)

Location: Remote, based in Warsaw, Poland

Company

Build an ML and MLOps scale-up inside InPost as part of the core Data & AI leadership function, with executive backing and ownership of models from initial concept through profit-and-loss impact.

What you will do

• Build and lead the ML & MLOps team from the first hire.
• Develop a state-of-the-art ML platform for serving, monitoring, reproducibility, and retraining.
• Own model-development standards, validation methodology, model-risk controls, and independent sign-off before production.
• Architect agentic ML systems with orchestration, guardrails, evaluations, human-in-the-loop checkpoints, and observability.
• Own the reliability, cost, and latency of production agent loops and graphs.
• Define ownership boundaries between feature production and model consumption with Data Engineering and Credit Risk partners.

Requirements

• Leadership experience building and leading ML teams from zero in regulated settings.
• Hands-on understanding of models, pipelines, and serving layers, especially when needed for AI systems.
• Experience across the full ML lifecycle: development, validation, deployment, monitoring, and retraining.
• Experience architecting agentic ML systems, including orchestration, guardrails, evaluations in CI, human-in-the-loop checkpoints, and agent observability.
• Experience in credit-scoring, underwriting, or comparable high-stakes ML modelling.
• Skills in model-risk management and responsible-AI governance.
• Fluent English at B2+ level.
• Ability to work with a point-in-time-correct feature store and model-consumption platform.

Nice to have

• Knowledge of CCD2 and consumer-credit regulation.
• Knowledge of DORA/ICT risk.
• Understanding of IFRS 9 implications for model outputs.
• Experience with fraud-detection ML.
• Experience with Databricks or Spark.

Culture & Benefits

• Remote work arrangement in Poland.
• Extreme ownership, with models tied to measurable monetary outcomes.
• Opportunity to build the ML function and platform without a legacy ML estate.
• Seat on a core leadership team in a lean, AI-native organisation.