Principal AI Platform 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 and Twitter (X)

As a Principal AI Platform Engineer in the Edge AI Solutions group at Analog Devices, you will be responsible for designing, building, and maintaining scalable and robust MLOps and DataOps capabilities to power our future AI platform for the Edge. The platform will enable the development and deployment of machine learning models for the Edge supporting the entire AI lifecycle, from edge-to-cloud data acquisition and ingestion to model training, deployment, and monitoring. You will work closely with machine learning and software engineers to ensure the platform meets the customer's needs and enables efficient ML and Data operations. 

Responsibilities: 

• Design and develop scalable AI platforms to support end-to-end Edge AI workflows and to handle large-scale data and compute workloads. 

• Build ML pipelines to support the development, experimentation, deployment, testing, and monitoring of Edge AI/ML models 

• Work closely with data scientists, software engineers, hardware engineers, and stakeholders to productize, deploy, and maintain Edge AI/ML models 

• Monitor the performance, security, and scalability of ML infrastructure and continuously explore ways to enhance data quality and reliability 

• Guide the MLOps and DataOps platform technology roadmap and identify opportunities to gain a competitive advantage 

• Stay up to date with the latest developments in ML and data technologies and tools 

Qualifications: 

• 8+ years of experience in software engineering, ML engineering, MLOps, or related fields, with proven experience in developing and maintaining scalable AI platforms. 

• Experience using MLOps, and DataOps frameworks such as Kubeflow, MLFlow, Airflow, etc.  

• Working knowledge of common ML frameworks such as TensorFlow, PyTorch, etc.,  

• Proficiency with one or more programming languages (Python preferred) 

• Expertise in containerization and orchestration