Senior MLOps Engineer - Anti-Fraud & Compliance Platform

WarsawRemoteDevopssenior

Machine Learning EngineerData

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Overview

Secure Global Money Transfers with Cutting-Edge Technology Join our mission to protect cross-border transactions, helping customers send money safely worldwide.

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Secure Global Money Transfers with Cutting-Edge Technology

Join our mission to protect cross-border transactions, helping customers send money safely worldwide.

As a Senior MLOps Engineer in our Anti-Fraud & Compliance team, you'll architect high-performance systems that detect fraud and enforce regulatory compliance.

Our ML stack focuses on tabular modeling for fraud detection and prevention.

Develop and implement a comprehensive MLOps strategy that enables the seamless integration of machine learning models into our environment.

Design, implement, and maintain scalable and automated MLOps pipelines (data ingestion, training, evaluation, deployment, and monitoring).

Build internal tools or integrate existing solutions for versioning, model registry, CI/CD, and observability.

Collaborate with cross-functional teams to design, deploy, and manage scalable infrastructure for machine learning workloads.

Own the full development lifecycle from design to incident response.

Work closely with data scientists, software engineers, and other stakeholders to understand model requirements, deployment needs, and data dependencies.

Enhance fraud detection systems using machine learning

5+ years of professional experience in MLOps or a related field.

Experience deploying and managing machine learning models in production environments.

Understanding ML lifecycle, model training and evaluation workflows, reproducibility, and model governance.

Experience building internal MLOps platforms or developer tools.

Experience with ML pipeline orchestration tools (e.g. Kubeflow, MLflow, Airflow, Metaflow, SageMaker Pipelines).

Knowledge of setting up CI/CD pipelines for ML workflows using GitHub Actions, GitLab CI, Argo, Jenkins, etc.

Experience deploying models in Docker/Kubernetes environments.

Strong knowledge of cloud platforms: AWS, GCP, or Azure.

Experience with setting up tools like Prometheus, Grafana for model & pipeline observability.

Strong programming skills in Python.

Experience with SQL/NoSQL databases and distributed systems.