Senior Machine Learning Engineer

Quick Facts

• Role: Senior Machine Learning Engineer

Description

Design, develop, and deploy machine learning models for credit scoring and merchant underwriting across the full ML lifecycle. Build point-in-time-correct features using a shared feature store to support BNPL and MCA decisioning, and create agentic workflows for reliable agent operations. Implement MLOps, monitor models in production, and ensure validation, explainability, and regulatory compliance (including CCD2) with automated backtesting and experimentation.

Responsibilities

• Design, develop, and deploy ML models for credit scoring and merchant underwriting

• Build point-in-time-correct features via a shared feature store for BNPL and MCA decisioning

• Collaborate with Data Engineering on data source contracts, data quality requirements, and data freshness SLAs

• Design and maintain agentic workflows (agent loops, orchestration graphs, context management, tool integrations, evaluation mechanisms, guardrails)

• Implement MLOps (model versioning, automated CI/CD, champion/challenger or shadow deployment)

• Monitor production models for data drift, population stability, and performance degradation; implement retraining, recalibration, or rollback

• Ensure explainability and regulatory compliance through documentation, validation evidence, and explainability techniques

• Support regulatory and audit requirements, including CCD2 creditworthiness assessment

• Build automated validation and backtesting frameworks integrated with the MLOps pipeline

• Develop automated experimentation, evaluation, retraining, and model promotion workflows

• Optimize reliability, observability, and operational costs of ML and agentic AI systems

Requirements

• Strong commercial experience in Machine Learning Engineering with production ML systems

• Hands-on full ML lifecycle experience (feature engineering, model training, validation, deployment, monitoring)

• Very good knowledge of Python and SQL

• Practical experience with Spark and/or Databricks in large-scale ML environments

• Strong understanding of model validation techniques (backtesting, PSI, Gini/KS metrics, calibration)

• Experience with MLOps including model registries, versioning, CI/CD pipelines, 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 explainability methods such as SHAP and LIME, and ability to prepare technical and validation documentation

• Experience building advanced agentic AI workflows (orchestration, loop and graph design, context management, tool integrations, guardrails, 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

Benefits

• Small team

• International working environment and international projects

• Private medical care

• Sports card

• Training courses

• Hybrid working model: preferably 1 day per week in the Warsaw or Cracow office