Associate Machine Learning Operations; MLOps Engineer

Associate Machine Learning Operations (MLOps) Engineer

About Analog Devices Analog Devices, Inc. (NASDAQ: ADI) is a global semiconductor leader bridging 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.

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As an entry‑level MLOps Engineer, you will build foundational skills while learning how core processes and tools support technical success in machine learning operations. You will independently design and optimize systems, resolve technical issues, and apply industry best practices for continuous improvement. You’ll help develop major ML/AI operational features spanning infrastructure, pipelines, deployment, monitoring, governance, and cost/risk optimization.

• Foster and contribute to a culture of operational excellence and proactive process enhancements to ensure enduring value and platform maturity.
• Build resilient cloud‑based ML/AI operational capabilities with learnability, flexibility, extensibility, interoperability, and scalability.
• Assist in setting up cloud resources (e.g., EC2 instances, S3 buckets, Sage Maker 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 monitoring of cloud resources and ML workflows by setting up basic monitoring tools or dashboards.
• Collaborate on compliance, 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, capturing results for review, and documenting testing processes.
• Learn and assist with GenAI/LLM‑based proofs‑of‑concepts and basic testing environments.
• Gain hands‑on experience with Kubernetes, managing clusters, and deploying sample ML workflows.
• Learn workflow orchestration tools (e.g., Argo, Kubeflow) and assist in setup and testing.
• Support creation and governance of simple data pipelines using Airflow.

• Basic understanding of the machine learning lifecycle (data preprocessing, model training, evaluation).
• Familiarity with cloud‑based services (AWS, Azure, or Google Cloud).
• Exposure to infrastructure‑as‑code tools (Terraform, AWS CDK) and workflow orchestration tools (Airflow or Kubeflow) is a plus.
• Experience with Python or Bash.
• Strong communication and documentation skills.
• A growth mindset and eagerness to learn new tools, platforms, and methodologies.

For positions requiring access to technical data, Analog Devices, Inc. may have to obtain export licensing approval. Applicants who are not U.S. Citizens, Permanent Residents, or protected individuals may need to go through an export licensing review.

Analog Devices is an equal opportunity employer. We foster a culture where everyone has an opportunity to succeed, regardless of race, color, religion, age, ancestry, national origin, gender, sexual orientation, gender identity, marital status, pregnancy, parental status, disability, medical condition, genetic information, veteran status, union membership, or political affiliation.

EEO is the law:
Notice of Applicant Rights Under the Law.

Senior level: Entry level

Employment type: Full‑time

Job function: Management and Manufacturing

Industries: Semiconductor Manufacturing

Shift: 1st Shift/Days

Travel: Yes, 10% of the time

Wage range: $69,600 – $95,700 (actual offer may vary)

Benefits: Medical, vision, dental, 401(k), paid vacation, holidays, sick time, discretionary performance‑based bonus, and other benefits.