Head of Machine Learning & MLOps Engineer

Quick Facts

• Role: Head of Machine Learning & MLOps Engineer (ML & MLOps leadership)

• Focus: Production ML platform, governed model development, agentic ML systems, and responsible AI for high-stakes decisioning

Description

You will lead and build an ML & MLOps team and establish engineering standards for a modern, production-ready ML platform. The role covers model serving, monitoring, reproducibility, automated retraining, and governance for credit-scoring/decisioning models and agentic ML systems. You will ensure reliability, performance, cost efficiency, and latency in production, while applying responsible AI controls and validation aligned with model-risk and regulatory needs.

Responsibilities

• Build and lead the ML & MLOps team from the ground up; recruit, develop, and set engineering standards

• Develop and maintain a production ML platform (model serving, monitoring, reproducibility, automated retraining)

• Design and oversee credit-scoring/decisioning models: validation, champion/challenger testing, and explainability

• Define model development and validation standards aligned with regulatory and model-risk requirements

• Design and implement agentic ML systems (orchestration, guardrails, CI-based evaluations, human-in-the-loop, observability)

• Manage agent-based model iteration, monitoring, and observability with integration to the governed feature store

• Ensure production reliability, performance, cost efficiency, and low latency for AI agents and workflows

• Implement responsible AI practices (bias detection, explainability, model-risk controls, independent approvals)

• Ensure point-in-time-correct, governed data is used for ML development from the feature store

• Coordinate responsibilities between feature engineering and model development with Data Engineering and leadership

• Manage ML function from a business perspective (compute budgets, headcount planning, ROI)

• Ensure each ML model has a clear business case and measurable financial impact

Requirements

• Proven experience building and managing Machine Learning and MLOps teams, ideally from initial stages

• Strong ML background across the full lifecycle (development, validation, deployment, monitoring, retraining)

• Hands-on knowledge of ML models, data pipelines, model serving, and production ML infrastructure

• Practical experience implementing agentic AI/ML systems (orchestration, guardrails, CI-based evaluations, human-in-the-loop, observability)

• Experience building and operating production-grade ML platforms

• Experience developing credit-scoring/underwriting/high-stakes decisioning models

• Knowledge of model-risk management, responsible AI, explainability, and governance

• Experience leading ML teams in regulated industries/environments

• Ability to establish technical standards and validation methodologies for reliable ML engineering

• Leadership skills with a hands-on engineering mindset

• Business-oriented mindset with ROI focus

• Very good English and Polish

Benefits

• 100% remote work (with option to use a different system); office available in Krakow

• International working environment and international projects

• Private medical care

• Sports card

• Training courses

• Small team

• Support for conferences and publication opportunities (including speaking)

• Direct communication with the board; founders are software developers and designers