Senior AI Engineer – Data, MLOps

🕒 August 25

🍂 Massachusetts – Remote

🍂 Massachusetts – Remote

💵 $133.9k - $223.9k / year

⏰ Full Time

🟠 Senior

🚰 Data Engineer

👻 Ghost score 8%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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Teradyne

WebsiteLinkedInAll Job Openings

5001 - 10000 employees

Founded 1960

🏭 Manufacturing

🔧 Hardware

🤝 B2B

Manufacturing • Hardware • B2B

Teradyne is a provider of test and manufacturing automation solutions. Its companies deliver manufacturing automation and automated test equipment across industries and applications, helping customers achieve higher production volumes, improved quality and greater ROI. Teradyne has expanded through acquisitions of specialists such as LitePoint (wireless test), Universal Robots (collaborative robots) and MiR (mobile robots), giving it a portfolio that spans semiconductor and electronics test, industrial robotics, and factory automation.

📋 Description

• Design, build, and operationalize machine learning and AI solutions for Teradyne’s IT organization • Translate business problems into 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 • Build and integrate AI agents with enterprise data sources, APIs, and MCP servers • Apply experimentation, model selection, and evaluation to deliver accurate and performant solutions • Own the transition of internally developed AI solutions from build to production • Define and certify release readiness according to AI Platform Operations Team standards • Implement model lifecycle management, including versioning, model registry, retraining, promotion, and deprecation • Define drift, degradation, and retraining criteria and drive tuning and remediation • Provide technical input for runbooks, on-call processes, and handoff standards • Engineer reusable, multi-environment MLOps/LLMOps pipelines • Build CI/CD, testing, and infrastructure/configuration-as-code for models, prompts, and agents • Establish automated offline and online evaluation and regression testing • Define technical standards, reference patterns, reusable frameworks, processes, and playbooks • Mentor and upskill engineers on ML/AI, MLOps/LLMOps, and agentic AI • Implement responsible AI practices, security controls, observability, logging, and audit trails • Monitor solution performance, quality, reliability, and cost and drive continuous improvement • Report to the Enterprise AI/Data Product Manager within Enterprise Architecture and Data

🎯 Requirements

• 8–10 years of experience in ML/AI or software engineering • At least 3 years building and operating production ML/AI systems • 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 • Experience with scikit-learn, PyTorch, or TensorFlow • Hands-on experience with Azure AI Foundry, Microsoft Copilot Studio, Anthropic Claude, Cursor, Google Vertex AI, or Snowflake Cortex AI • Proven MLOps/LLMOps experience, including 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, 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 • Experience integrating with enterprise data on Snowflake and Microsoft Fabric • Proven ability to mentor and upskill teams • Strong collaboration and communication skills across technical and business teams • Analytical mindset focused on measurable business outcomes

🏖️ Benefits

• Discretionary bonus(es) based on financial performance • Medical insurance • Dental insurance • Vision insurance • Flexible Spending Accounts • Retirement savings plans • Life insurance • Disability insurance • Paid vacation and holidays • Tuition assistance programs