Associate Machine Learning Operations (MLOps) Engineer

Analog Devices•Wilmington, MA

23h

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, 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 $9 billion in FY24 and approximately 24,000 people globally, ADI ensures today's innovators stay Ahead of What's Possible™. Learn more at www.analog.com and on LinkedIn and Twitter (X). This role is an entry-level position within the global XOps team — which includes MLOps, LLMOps, AgentOps, and DevOps — whose mission is to deliver a world-class AI/ML developer experience for our software engineers and data scientists. You will join a mission-driven interdisciplinary team that spans data science, software engineering, product management, cloud architecture, and security expertise. A team that believes in a culture founded on trust, mutual respect, growth mindsets, and an obsession for building extraordinary products with extraordinary people. As an entry-level MLOps Engineer, you will start building your foundational skills while learning how core processes and tools support technical success in machine learning operations. You will independently design and optimize complete systems, resolve technical issues via systematic analysis, and apply industry best practices and advanced methodologies for continuous improvement. You’ll help develop major ML/AI operational features that span multiple aspects of the ML/AI developer experience— from infrastructure to pipelines, deployment, monitoring, governance, and cost/risk optimization.

Requirements

Basic understanding of the machine learning lifecycle (e.g., data preprocessing, model training, evaluation).
Familiarity with cloud-based services (e.g., AWS, Azure, or Google Cloud).
Experience with programming languages such as Python or Bash.
Strong communication and documentation skills.
A growth mindset and eagerness to learn new tools, platforms, and methodologies.

Nice To Haves

Exposure to infrastructure-as-code tools (e.g., Terraform, AWS CDK) and workflow orchestration tools (e.g., Airflow or Kubeflow) is a plus.

Responsibilities

Foster and contribute to a culture of operational excellence: high-performance, mission-focused, interdisciplinary collaboration, trust, and shared growth.
Help drive proactive capability and process enhancements to ensure enduring value creation, analytic compounding interest, and operational maturity of the ML/AI platform.
Help build resilient cloud-based ML/AI operational capabilities that advance our system attributes: learnability, flexibility, extensibility, interoperability, and scalability.
Assist in setting up cloud resources (e.g., EC2 instances, S3 buckets, and SageMaker environments) to support the lifecycle of ML models and services.
Learn and apply foundational concepts of cloud architecture under the guidance of senior engineers.
Document configuration steps and contribute to maintaining infrastructure scripts for scalability and reliability.
Support efforts to monitor cloud resources and ML workflows by setting up basic monitoring tools or dashboards.
Collaborate with the team to ensure compliance by following pre-defined frameworks and flagging any issues or inconsistencies.
Gain exposure to infrastructure lifecycle management concepts, such as drift detection and provisioning.
Assist in testing and validating ML pipelines by running test cases and capturing results for review.
Contribute to quality assurance efforts by creating detailed documentation of testing processes and outcomes.
Learn how GenAI/LLM-based proofs-of-concept are evaluated and assist with basic tasks, such as setting up testing environments.
Gain hands-on experience with Kubernetes by helping manage clusters and working on tasks like deploying sample ML workflows.
Learn about workflow orchestration tools (e.g., Argo, Kubeflow) by assisting in their setup and testing under supervision.
Support the creation and governance of simple data pipelines using tools like Airflow.