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 Senior Machine Learning Engineer (FinTech / MLOps / Agentic AI).
Your duties will include:
- Designing, developing, and deploying machine learning models for credit scoring and merchant underwriting, covering the entire ML lifecycle.
- Building point-in-time-correct features using a shared feature store to support BNPL (Buy Now, Pay Later) and MCA (Merchant Cash Advance) decisioning.
- Cooperating with Data Engineering teams to establish data source contracts, quality requirements, and data freshness SLAs.
- Designing and maintaining agentic workflows, including agent loops, orchestration graphs, context management, tool integrations, evaluation mechanisms, and guardrails.
- Implementing MLOps processes, including model versioning, automated CI/CD pipelines, and champion/challenger or shadow deployment strategies.
- Monitoring production models for data drift, population stability, and performance degradation, and implementing retraining, recalibration, or rollback mechanisms.
- Ensuring model explainability and regulatory compliance through appropriate documentation, validation evidence, and explainability techniques.
- Supporting regulatory and audit requirements, including CCD2 creditworthiness assessment.
- Developing automated validation and backtesting frameworks integrated with the MLOps pipeline.
- Building and maintaining automated experimentation, evaluation, retraining, and model promotion workflows.
- Optimizing the reliability, observability, and operational costs of ML and agentic AI systems
Requirements that must be met:
- Strong commercial experience in Machine Learning Engineering, including building and operating ML solutions in production environments.
- Hands-on experience across the full ML lifecycle: feature engineering, model training, validation, deployment, and monitoring.
- Very good knowledge of Python and SQL.
- Practical experience with Spark and/or Databricks in large-scale machine learning environments.
- Strong understanding of model validation techniques, including backtesting, Population Stability Index (PSI), Gini/KS metrics, and model calibration.
- Experience with MLOps, including model registries, versioning, CI/CD pipelines, model monitoring, drift detection, and automated alerting.
- Knowledge of champion/challenger testing and shadow deployment strategies.
- Practical experience with feature stores and point-in-time-correct feature engineering.
- Familiarity with model explainability methods such as SHAP and LIME, as well as preparing technical and validation documentation.
- Experience in developing advanced agentic AI workflows, including agent orchestration, loop and graph design, context management, tool integration, guardrails, and evaluation frameworks.
- Good understanding of Large Language Models (LLMs) and their practical application in engineering workflows.
- Ability to design and deliver reliable, observable, and cost-efficient ML systems.
- Strong analytical skills, independence, and a hands-on approach to problem-solving.
- Ability to collaborate effectively with engineering, data, and business teams.
- Very good English and Polish