What are the responsibilities and job description for the Machine Learning Engineer position at ExtendMyTeam?
The Risk & Fraud team helps customers take a proactive stance against fraud while managing the risks inherent to their business. We build and enhance products that evolve with the ever-changing fraud landscape, delivering tangible value to customers. Our solutions allow financial institutions to focus more of their time and energy on serving their customers and communities. As a Machine Learning Engineer, you will help build and operate production systems that power fraud detection and risk-related products. You’ll work closely with data scientists and engineers to bring models into production, ensuring they are reliable, scalable, and maintainable. You’ll gain hands-on experience working across model development, evaluation, deployment, monitoring, and ongoing improvements. This is an applied engineering role — the software you build will solve real-world problems and must be production-ready, reliable, and testable. A Typical Day Your Key Responsibilities Build and maintain systems and pipelines that support training, evaluation, and inference for machine learning models Contribute to deploying machine learning models into production environments and ensuring they run reliably at scale Write clean, maintainable, and well-tested code following production engineering best practices Support monitoring and troubleshooting production ML systems, including data pipelines and model performance Collaborate with data scientists and engineers to productionalize models and integrate them into scalable applications Help improve the reliability, scalability, and performance of ML systems over time Contribute to improving tooling and infrastructure that supports the ML development lifecycle You Are More Likely to Excel If You: Enjoy autonomy in your work and take ownership of team goals while balancing speed with long-term impact Have empathy for end users and measure success through both customer value and technical quality Are enthusiastic about machine learning, engineering excellence, and continuous professional development Bring Your Passion, Do What You Love. Here’s What We’re Looking For Must-Haves Bachelor’s degree in a relevant field with 2 years of related experience, or equivalent practical experience Proficiency in Python Experience writing clean, maintainable code and using version control tools such as Git Experience with machine learning frameworks such as PyTorch, TensorFlow, or scikit-learn Nice to Have Experience building end-to-end ML systems, including data pipelines, model training, deployment, and monitoring Experience deploying or integrating machine learning models into applications Experience building APIs, backend services, or working with distributed systems Familiarity with cloud platforms such as AWS, GCP, or Azure Exposure to MLOps concepts such as CI/CD and model monitoring Experience working with large datasets or data processing frameworks Experience with additional programming languages such as TypeScript