Senior AI Software Engineer

Careers that change lives start here. Medtronic is a global leader in healthcare technology with a Mission to alleviate pain, restore health, and extend life. Our 95,000 employees work across more than 150 countries to put patients first — developing innovative medical technologies that improve the lives of 72+ million patients each year. Your unique talents will help shape the future of healthcare while building a career grounded in purpose, growth, and impact. A Day in the Life At Medtronic, we value what makes you unique. Be part of a company that thinks differently to solve problems, make progress, and deliver meaningful innovations.  Our Purpose     The Interventional Cardiology Therapies (ICT) Operating Unit is dedicated to transforming care for patients with coronary disease and hypertension. We combine innovative devices, digital technologies, and advanced analytics and artificial intelligence to support clinicians and improve patient outcomes worldwide. The work you do will impact patients globally and help support Medtronic’s mission of alleviating pain, restoring health, and extending life.  Come for a job, stay for a career!    A Day in The Life Of:    As a Senior AI Software Engineer , you will combine strong data science expertise with solid software engineering skills to design, build, and deploy production-grade machine learning systems. This role focuses on developing robust ML models and integrating them into scalable software systems that support real-world applications. The ideal candidate has a strong theoretical foundation in machine learning and data science, along with hands-on experience building ML pipelines, deploying models to production environments, and maintaining reliable ML services. This role works closely with data engineers, software engineers, and product teams to operationalize AI solutions.

Computer Vision & Machine Learning Development  Design, develop, and evaluate computer vision and machine learning models to solve complex business and technical problems.  Perform data exploration, feature engineering, and model experimentation.  Apply appropriate validation and evaluation methodologies to ensure robust model performance.  Continuously improve models through experimentation and iterative development.  ML Systems and Production Deployment  Build end-to-end machine learning pipelines for data ingestion, training, validation, and deployment.  Deploy models into production environments as APIs, services, or batch processing pipelines.  Implement monitoring and performance tracking for deployed models.  Ensure reproducibility, versioning, and lifecycle management of ML models.  Software Engineering  Develop high-quality, maintainable, and testable Python code for ML systems. Apply software engineering best practices including modular design, automated testing, and code review. Design scalable services and components that integrate ML models into production applications. Collaborate with engineering teams to integrate ML capabilities into broader systems. Infrastructure and MLOps  Work with cloud-based infrastructure to support ML training and deployment workflows.  Implement CI/CD processes for ML pipelines and model releases.  Support automation of model retraining and operational monitoring.  Ensure reliability, scalability, and observability of ML services.  Collaboration and Communication  Work in agile/scrum framework and cross-functionally with data engineers, software engineers, and domain experts.  Translate analytical results into practical solutions and production-ready systems.  Contribute to architectural discussions for AI-enabled platforms and products.

Master’s Degree in Computer Science, Data Science, Artificial Intelligence, Machine Learning, or a related field. Strong theoretical foundation in machine learning, statistics, and data science methods. Demonstrated experience developing