Machine Learning Engineer

• Develop full-stack machine learning models, including binary classification and regression, for financial crime detection and prevention during transaction processing and client onboarding
• Gather requirements, prototype solutions, and deploy models in production
• Own and evolve the Dynamic Risk Score (DRS) across all markets
• Score the full client base daily and support decisioning across the client lifecycle
• Manage transaction rules with ML-driven detection and optimize alert performance and volume
• Establish continuous production monitoring for model and rule performance
• Develop the champion-challenger framework to keep the best-performing model live
• Participate in infrastructure development for ML model operationalization and inter-service interactions
• Deploy, serve, monitor, and improve production ML systems on a small team

Senior Machine Learning Engineer

• Design and build core machine learning systems for a proactive, forward-looking AI platform
• Own projects end-to-end, including data curation, model training, evaluation, inference, and continuous iteration
• Translate cutting-edge research concepts into robust, reliable production systems
• Use live production signals and data to troubleshoot model anomalies and system bottlenecks
• Deploy systems, analyze real-world performance metrics, refine approaches, and iterate rapidly
• Collaborate with research, engineering, and product teams to connect technical milestones with user value
• Mentor fellow engineers and conduct rigorous technical reviews
• Optimize model architectures for latency, computational cost, reliability, and safety at scale

• Design, develop, and maintain scalable data and end-to-end ML pipelines, from data ingestion to production deployment
• Build and deploy ML services and APIs, ensuring reliability, scalability, and performance
• Partner with Data Scientists to transform models into robust, production-ready solutions
• Implement MLOps best practices, including CI/CD, testing, data/code quality, monitoring, and model lifecycle management
• Troubleshoot production ML systems and drive technical and architectural decisions
• Work with AWS ML services, particularly SageMaker, and containerized environments using Docker/Kubernetes
• Contribute to GenAI implementations within the platform framework
• Mentor team members and contribute to delivery in an Agile/Scrum environment

• Reliably deploy machine learning models developed by data scientists into production environments
• Develop and support ML-focused CI/CD deployment pipelines
• Monitor production models, including performance, failures, and key metrics
• Implement basic model alerting where needed
• Write clean, structured, maintainable, and testable Python code
• Collaborate with data scientists and data engineers on model deployment and data transformation initiatives
• Optimize model response times and operational efficiency
• Manage production models, deployments, pipelines, and production fixes
• Pursue retraining automation initiatives

Machine Learning Engineer jobs in Portugal

Senior Machine Learning Engineer

Senior Machine Learning Engineer

Senior Machine Learning Engineer

Machine Learning Engineer – MLOps