Head of Machine Learning & MLOps Engineer

Hello,
We are called People More because we treat our employees with respect, but also because the projects we work on are for people and should be easy and pleasant to use. We are technological, but we look at the bigger picture :)
The company is made up of people with a huge client base in the country and abroad, for whom we build projects from scratch (UX, UI, frontend, backend, mobile) or in part. We work directly for our clients and also support our partners in their own solutions. This ensures a wide range of projects and the ability to change! We work with clients all over the world.
For the project that we are working on with our partner, we are looking for Head of Machine Learning & MLOps Engineer.

Your duties will include:

• Building and leading the ML & MLOps team from the ground up, including recruitment, team development, and establishing engineering standards.

• Developing a modern, production-ready ML platform covering model serving, monitoring, reproducibility, and automated retraining.

• Designing and overseeing credit-scoring and decisioning models, including rigorous validation, champion/challenger testing, and explainability.

• Establishing model development standards and validation methodologies aligned with regulatory and model-risk requirements.

• Designing and implementing agentic ML systems, including orchestration, guardrails, automated evaluations, human-in-the-loop checkpoints, and observability.

• Managing agent-based model iteration, monitoring, and observability, ensuring integration with the governed feature store.

• Ensuring the reliability, performance, cost efficiency, and latency of AI agents and workflows running in production.

• Implementing responsible AI practices, including bias detection, explainability, model-risk controls, and independent approval processes before production deployment.

• Ensuring ML models are developed using governed, point-in-time-correct data from the feature store.

• Working closely with the Head of Data Engineering and the Director to define responsibilities between feature engineering and model development.

• Managing the ML function from a business perspective, including compute budgets, headcount planning, and return on investment.

• Ensuring that each ML model has a clearly defined business case and measurable financial impact

Requirements that must be met:

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

• Strong technical background in machine learning and experience working 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