Machine Learning Engineer

Analog Devices, Inc. (NASDAQ: ADI) is a global semiconductor leader that bridges the physical and digital worlds to enable breakthroughs at the Intelligent Edge. ADI combines analog, digital, and software technologies into solutions that help drive advancements in digitized factories, mobility, and digital healthcare, combat climate change, and reliably connect humans and the world. With revenue of more than $12 billion in FY23 and approximately 26,000 people globally working alongside 125,000 global customers, ADI ensures today’s innovators stay Ahead of What’s Possible. Learn more at www.analog.com and on LinkedIn.

Position Overview:  

We are seeking an experienced Machine Learning Engineer to join our Enterprise AI Enablement team. In this role, you will collaborate with cross-functional teams to transform business challenges into ML-powered solutions. You will be responsible for the end-to-end development of machine learning projects, from conceptualization to deployment and maintenance. This position offers the opportunity to work on diverse projects across enterprise business units while building scalable ML solutions. 

Key Responsibilities: 

• Design and implement machine learning solutions that address complex business problems across different enterprise business units 

• Collaborate with business stakeholders to understand requirements and translate them into technical specifications 

• Create robust prototypes to validate AI concepts and transform them into production-ready systems 

• Design, develop and deploy end-to-end MLOps pipelines, including data preprocessing, hyper parameter tuning, model training, evaluation, and production deployment 

• Implement best practices for model monitoring, maintenance, and performance optimization 

• Conduct thorough testing and validation of ML models to meet the standards performance KPI metrics to ensure reliability and accuracy 

• Document technical processes, methodologies, model governance, and model architectures 

Required Qualifications: 

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, or a related field. A Ph.D. is a plus. 

• Minimum 5 years of professional experience in machine learning engineering 

• Experience with the Infrastructure Sizing, Cost Management, Budget Forecasting and Reporting, and Workflow Optimization 

• Strong programming skills in Python and experience with ML frameworks (e.g., TensorFlow, PyTorch, scikit-learn) 

• Proven experience in developing and deploying production grade ML models in both conventional and deep learning techniques 

• Proficiency in data preprocessing, feature engineering, and model evaluation techniques 

• Experience with version control systems (e.g., Git) and ML experiment tracking tools 

• Strong understanding of statistical analysis and machine learning algorithms