Principal Engineer, AI/ML Software

Analog Devices•Boston, MA

2d•Hybrid

About The Position

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, AI, and software technologies into solutions that combat climate change, reliably connect humans and the world, and help drive advancements in automation and robotics, mobility, healthcare, energy and data centers. With revenue of more than $11 billion in FY25, ADI ensures today's innovators stay Ahead of What's Possible. Learn more at www.analog.com and on LinkedIn and X.

Requirements

Bachelor’s degree in Computer Science, Information Technology, or a related field (or foreign education equivalent) and five (5) years of experience as a software engineer building and maintaining machine learning software workflows.
Master’s degree in Computer Science, Information Technology, or a related field (or foreign education equivalent) and three (3) years of experience as a software engineer building and maintaining machine learning software workflows.
Demonstrated Expertise designing, developing, and maintaining end-to-end machine learning (ML) pipelines, including data ingestion, preprocessing, model training, validation, and deployment (using PyTorch or TensorFlow); and managing experiment tracking and model lifecycle with MLflow or CometML.
Demonstrated Expertise in technical leadership of production ML platforms and pipelines—leading a small, cross-functional team; setting standards, running design/code reviews, and mentoring junior engineers.
Demonstrated Expertise building scalable systems on cloud platforms, with hands-on experience designing fault-tolerant architectures, distributed training setups, multi-cloud strategies (using AWS, GCP, or Azure), and automating infrastructure tasks with Linux and shell scripting.
Demonstrated Expertise in containerization, orchestration, and MLOps/DevOps practices, including deploying ML models and pipelines with Docker and Kubernetes; implementing CI/CD and infrastructure-as-code (Terraform or AWS CloudFormation); and setting up monitoring and observability (Prometheus and Grafana).
Demonstrated Expertise developing distributed data processing pipelines for real-time or batch ML workflows (using Apache Airflow and Apache Kafka).
Demonstrated Expertise leading the design, building, and maintenance of scalable, robust, and secure RESTful APIs and microservices architectures using Python, with knowledge of computer networks and protocols.

Responsibilities

Design, build, and maintain robust MLOps (Machine-Learning Operations) software systems.
Support the development, deployment, testing, and monitoring of AI/ML models on modern cloud-native platforms.
Collaborate with data scientists, software engineers, and stakeholders to operationalize AI/ML solutions and ensure their production readiness.
Implement and maintain ETL pipelines, automated workflows, and scalable data stores.
Ensure high standards of model performance, security, and scalability through continuous monitoring and enhancement of software infrastructure.
Guide the MLOps technology roadmap and evaluate emerging tools and technologies to enhance platform capabilities.
Utilize MLOps frameworks such as Kubeflow and MLflow, and work with containerization and orchestration tools including Docker and Kubernetes.
Deploy infrastructure using Terraform and manage cloud-based resources on platforms such as GCP, AWS, and Azure.
Contribute to Agile development processes and cross-functional team collaboration.