Senior AI Engineer - Data & MLOps (Teradyne, Remote)

Teradyne•North Reading, MA

9d•$133,900 - $223,900•Remote

About The Position

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. 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.

Requirements

8–10 years of experience in ML/AI or software engineering, including at least 3 years building and operating production ML/AI systems and recent hands-on experience with generative and agentic AI.
Bachelor’s or advanced degree in Computer Science, Data Science, Engineering, or a related field.
Strong proficiency in Python and SQL, and experience with ML frameworks such as scikit-learn, PyTorch, or TensorFlow.
Hands-on experience building and deploying ML and generative AI solutions with one or more enterprise AI platforms: Azure AI Foundry, Microsoft Copilot Studio, Anthorpic Claude, Cursor, Google Vertex AI, and Snowflake Cortex AI.
Proven MLOps/LLMOps experience—multi-environment pipelines, CI/CD (Azure DevOps or GitHub Actions), Azure Machine Learning, MLflow, and model lifecycle management.
Experience with agentic AI orchestration frameworks, RAG, vector databases and embeddings, prompt engineering, and integration via MCP servers and APIs.
Experience transitioning AI solutions from development into production ownership, including monitoring, drift detection, retraining, and support.
Strong knowledge of model evaluation and testing, AI observability, responsible AI, AI security, and governance frameworks.
Multicloud experience with Microsoft Azure as primary and Google Cloud as secondary, integrating with enterprise data on Snowflake and Microsoft Fabric.
Poven ability to mentor and upskill teams, fostering a culture of innovation and learning.
Strong collaboration and communication skills, with the ability to work across technical and business teams.
Analytical mindset with a focus on delivering measurable business outcomes.

Responsibilities

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.
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.
Engineer reusable, multi-environment MLOps/LLMOps pipelines using Azure DevOps or GitHub 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 auditable.
Leverage tooling such as Azure Machine Learning, MLflow, and model/feature registries to standardize experimentation, tracking, and deployment.
Establish automated evaluation and regression testing (including offline and online evaluation) for both predictive and generative solutions.
Design MLOps/LLMOps pipelines for the AI Platform Operations Team to operate in production once deployed, ensuring pipelines are supportable, observable, and handoff-ready.
Establish the technical standards, reference patterns, and reusable frameworks the team needs to build and operate AI solutions consistently.
Define and document the processes and playbooks—development, evaluation, deployment, monitoring, and support—that prepare the team to own AI solutions end-to-end.
Mentor and upskill engineers on ML/AI, MLOps/LLMOps, and agentic AI practices, raising the overall capability and readiness of the team.
Champion adoption of best practices and lessons learned across projects to mature the team’s AI operating model.
Implement responsible AI practices and technical guardrails for data handling, model usage, and safe, compliant AI behavior.
Apply AI security controls to models, agents, and RAG workflows, mitigating risks such as prompt injection and unauthorized data access.
Build observability, logging, and audit trails for AI solutions to support explainability, compliance, and troubleshooting.
Monitor solution performance, quality, reliability, and cost, and drive continuous improvement against defined metrics.