Head of Machine Learning & MLOps Engineer

150 - 200 PLN/ godz./ godz.B2BB2B (netto)

C-Level / ManagerZdalnie·KrakówFull-time·B2B

#476451·Dodano wczoraj·1

Źródło: People More

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Tech Stack / Keywords

Machine LearningMLOpsAIfeature storemodel servingdata pipelinesCIhuman-in-the-loop

Podsumowanie oferty

You will build and lead the ML & MLOps team and develop a production-ready ML platform. The role also covers credit-scoring and decisioning models and agentic AI systems for a partner project.

Wymagania

Requirements that must be met:

Proven experience building and managing Machine Learning and MLOps teams, ideally from the initial stages.
Strong technical background in machine learning and experience across the full ML lifecycle: development, validation, deployment, monitoring, and retraining.
Hands-on understanding of ML models, data pipelines, model serving, and production ML infrastructure.
Practical experience designing and implementing agentic AI/ML systems, including orchestration, guardrails, CI-based evaluations, human-in-the-loop mechanisms, and agent observability.
Experience building and operating production-grade ML platforms.
Experience developing credit-scoring, underwriting, or other high-stakes decisioning models.
Strong knowledge of model-risk management, responsible AI, explainability, and governance.
Experience leading ML teams in regulated industries or environments.
Ability to establish technical standards, validation processes, and reliable ML engineering practices.
Strong leadership skills combined with a hands-on, engineering-oriented approach.
Business-oriented mindset and ability to connect ML initiatives to measurable financial outcomes and ROI.
Very good English and Polish.

Obowiązki

Your duties will include:

Build and lead the ML & MLOps team, including recruitment, team development, and engineering standards.
Develop a production-ready ML platform for model serving, monitoring, reproducibility, and automated retraining.
Design and oversee credit-scoring and decisioning models, including validation, champion/challenger testing, and explainability.
Establish model development standards and validation methodologies aligned with regulatory and model-risk requirements.
Design and implement agentic ML systems, including orchestration, guardrails, automated evaluations, human-in-the-loop checkpoints, and observability.
Manage agent-based model iteration, monitoring, and observability, integrating with the governed feature store.
Ensure the reliability, performance, cost efficiency, and latency of production AI agents and workflows.
Implement responsible AI practices, including bias detection, explainability, model-risk controls, and independent approval before production deployment.
Ensure ML models use governed, point-in-time-correct data from the feature store.
Work with the Head of Data Engineering and the Director to define responsibilities between feature engineering and model development.
Manage the ML function, including compute budgets, headcount planning, and return on investment.
Ensure each ML model has a defined business case and measurable financial impact.

Benefity

Private medical care.
Sports card.
Training courses.
Opportunity to attend conferences, including as a speaker, and support with publishing in recognized magazines and online.

Opieka zdrowotna

Karta sportowa

Inne informacje

You can be located anywhere.
The recruitment process consists of a remote initial interview, a remote technical talk, and a decision to work together.

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