Machine Learning ops engineer

Company Description
Renesas is one of the top global semiconductor companies in the world. We strive to develop a safer, healthier, greener, and smarter world, and our goal is to make every endpoint intelligent by offering product solutions in the automotive, industrial, infrastructure and IoT markets. Our robust product portfolio includes world-leading MCUs, SoCs, analog and power products, plus Winning Combination solutions that curate these complementary products. We are a key supplier to the world's leading manufacturers of electronics you rely on every day; you may not see our products, but they are all around you.
Renesas employs roughly 21,000 people in more than 30 countries worldwide. As a global team, our employees actively embody the Renesas Culture, our guiding principles based on five key elements: Transparent, Agile, Global, Innovative, and Entrepreneurial. Renesas believes in, and has a commitment to, diversity and inclusion, with initiatives and a leadership team dedicated to its resources and values. At Renesas, we want to build a sustainable future where technology helps make our lives easier. Join us and build your future by being part of what's next in electronics and the world.
About this role
As an MLOps Engineer, you will automate and streamline the process of integrating and maintaining machine learning models for both traditional and generative AI.
About our team
Our global AI, Data & Analytics Division at Renesas is experiencing an increasing demand for AI and Generative AI (LLM) based solutions. Working closely with our business partners, we deliver AI solutions to help drive value for Renesas at a global enterprise scale. We are seeking passionate candidates that will thrive as part of a global team of technologists that love empowering customers, collaborating with teammates, and using the latest AI and Data technologies.
Job Description
Developing, testing, deploying, and monitoring scalable and reliable machine learning pipelines using Databricks
Troubleshooting and resolving issues related to data, model, and infrastructure performance and availability
Optimize and monitor the performance, reliability, and security of AI/ML systems
Research and evaluate new technologies and frameworks for improving the efficiency and effectiveness of machine learning workflows
Applying best practices and standards for data quality, code quality, version control, CI/CD, and documentation