Description
The client is a global financial services group.
Responsibilities
•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
Requirements
•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
Nice to have
•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
Conditions
•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
