Senior AI Engineer - Data & MLOps Remote

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Date: Aug 25, 2026

Location: North Reading, MA, US

We are the global test and automation specialists, powering next-generation technologies through sophisticated solutions. Behind every electronic device you use, Teradyne's test technology ensures your device works right the first time, every time! Our portfolio of automation solutions help manufacturers to develop and deliver products quickly, efficiently and cost-effectively. Together,Teradyne companies deliver manufacturing automation across industries and applications around the world!

We attract, develop, and retain a high-performance workforce, comprised of people with diverse backgrounds and a shared drive for excellence. We strive to foster a positive and inclusive work environment that helps employees, and communities, thrive.

TERADYNE, where experience meets innovation and driving excellence in every connection. We are fueled by creativity and diversity of thought and in our workforce. Our employees are supported to innovate and learn something new every day.

We cultivate a culture of inclusion for all employees that respects their individual strengths, views, and experiences. We believe that our differences enable us to be a better team - one that makes better decisions, drives innovation and delivers better business results.

Opportunity Overview

As a Senior ML / AI Engineer at Teradyne, you will design, build, and operationalize the machine learning and AI solutions that power our IT organization, and you will prepare the team—technically and operationally—to own the AI solutions Teradyne develops internally. Reporting to the Enterprise AI/Data Product Manager within the Enterprise Architecture and Data organization, you will bridge solution development and production ownership, ensuring our AI solutions are reliable, governed, and sustainable long after they are first built.

This is a deeply technical, hands-on role spanning classical machine learning and modern generative and agentic AI. You will develop ML/AI solutions requested by the IT organization - predictive and classification models, GenAI assistants, RAG workflows, and intelligent automation—and engineer the MLOps/LLMOps practices, pipelines, and playbooks that allow the team to operate and continuously improve them. You will work across our enterprise AI stack - primarily Microsoft Azure (Azure AI Foundry and Microsoft Copilot Studio), Anthropic Claude, and Google Vertex AI and Snowflake Cortex AI - grounding solutions in trusted enterprise data and integrating with enterprise systems through MCP servers and secure APIs.

Your work ensures Teradyne's AI investments move from experimentation to durable, production-grade capabilities.

• Partner with the AI Enablement Team and the IT organization to translate business problems into ML/AI solutions - predictive and classification models, GenAI assistants, RAG workflows, and intelligent process automation.
• Design, develop, train, evaluate, and deploy end-to-end ML and generative AI solutions using Azure AI Foundry, Microsoft Copilot Studio, Google Vertex AI, and Snowflake Cortex AI.
• Build and integrate AI agents with enterprise data sources, APIs, and MCP servers, grounding models in proprietary Teradyne data through embedding and retrieval pipelines.
• Apply rigorous experimentation, model selection, and evaluation to deliver solutions that are accurate, performant, and fit for purpose.

Business Outcome: Deliver high-value, production-ready ML/AI solutions that solve real IT and business problems and demonstrate measurable impact.

• Own the transition of internally developed AI solutions from build to production, defining and certifying the release-readiness in accordance to standard set by the AI Platform Operations Team.
• Implement full model lifecycle management - versioning, model registry, retraining, promotion, and deprecation - so solutions remain accurate and maintainable over time.
• Define drift, degradation, and retraining criteria for deployed models and agents, and drive tuning and remediation of the underlying solution for the AI Platform Operations Team to execute scheduled retraining and registry operations against these criteria.
• Partner with the AI Platform Operations Engineer, providing the technical input - model and agent behavior, dependencies, failure modes - needed for the run-books, on-call processes, and handoff standards they own and author.

Business Outcome: Ensure internally developed AI solutions are durable, well-owned, and continuously improved rather than one-off builds.

• Engineer reusable, multi-environment MLOps/LLMOps pipelines using Azure Dev Ops or Git Hub Actions to automate the full lifecycle from training and evaluation to deployment.
• Build CI/CD, testing, and infrastructure-/configuration-as-code for models, prompts, and agents to make releases repeatable, testable, and…