ml engineer in financial services

The client is a global financial services group.

• Design, develop, and productionise end-to-end machine learning models, including training, validation, deployment, monitoring, and retraining
• Lead business-critical AI use cases such as client lifetime value, churn prediction, and fraud or abuse detection, aligned with measurable business outcomes
• Improve models through feedback loops, monitoring insights, and retraining strategies
• Establish MLOps practices, including deployment pipelines, CI/CD, environment promotion, and lifecycle management
• Implement model monitoring for performance, data drift, data quality, and business impact, with retraining and escalation strategies
• Apply model deployment, monitoring, drift detection, and retraining in production systems
• Ensure model explainability and transparency using SHAP, feature attribution, and other appropriate interpretability methods
• Define and enforce model governance, documentation, versioning, and auditability practices
• Collaborate with Data Engineering on data pipelines, feature engineering, reproducibility, and scalable data foundations
• Work with Product, Risk, Commercial, and other stakeholders to translate business problems into pragmatic AI solutions
• Mentor team members and promote best practices in production AI, MLOps, and applied machine learning delivery
• Own applied AI delivery from experimentation through production

• 5–8+ Years of experience building and deploying machine learning models in production environments
• Strong Python skills and software engineering fundamentals, including testing, code quality, modular design, and maintainability
• Strong understanding of machine learning, model evaluation, feature engineering, and production considerations such as data leakage, drift, and stability
• Hands-on experience with large-scale data processing using Spark or PySpark
• Experience with ML lifecycle tools such as MLflow for experiment tracking, model management, and reproducibility
• Experience building and maintaining CI/CD pipelines for ML or data workflows
• Strong SQL skills and experience with large, complex real-world datasets
• Proven ability to deliver AI/ML solutions with measurable business impact, not just model performance improvements
• Strong communication skills and ability to explain trade-offs to technical and non-technical stakeholders
• Ability to work with evolving requirements, imperfect data, and delivery pressure while balancing MVP speed and production robustness
• Будет плюсом: fintech, trading, or financial services experience; real-time or streaming ML systems; LLMs, embeddings, or RAG; regulated environments and model governance frameworks; contributing to team standards, mentoring, or leading applied AI delivery

• Remuneration package based on experience
• Training and development opportunities, team events, and birthday and loyalty benefits
• Stebby sports compensation or Confido health compensation in Estonia