Head of Machine Learning & MLOps Engineer

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

C-Level / ManagerZdalnie·KrakówInne·B2B

#476483·Dodano wczoraj·0

Źródło: justjoin.it

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Tech Stack / Keywords

Machine LearningAIManagement

Podsumowanie oferty

Lead the build-out of the ML & MLOps team and a production-ready ML platform for a partner project. The role covers credit-scoring and decisioning models, as well as designing and operating agentic AI systems.

Wymagania

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, with the ability to connect ML initiatives to measurable financial outcomes and ROI.
Very good English and Polish.

Obowiązki

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, and ensure integration with the governed feature store.
Ensure the reliability, performance, cost efficiency, and latency of AI agents and workflows in production.
Implement responsible AI practices, including bias detection, explainability, model-risk controls, and independent approval processes 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’s 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.
Support with publishing in recognized magazines and online.

Opieka zdrowotna

Karta sportowa

Dofinansowanie szkoleń

Inne informacje

The recruitment process includes a friendly remote initial interview and a remote technical talk.

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