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

- **Company:** TALENTHOP LLC
- **Location:** United States (Remote available)
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Application Frameworks, ARM Architecture, Continuous Integration, Machine Learning, Regression Testing, Runbook, Software Deployment, Systems Integration, Management of Software Versions, Enterprise Data Management, Snowflake, Google Vertex AI, Model Validation, Generative AI, Agentic-AI, Microsoft Copilot Studio, AI Platforms, Machine Learning Operations
- **Published:** October 3, 2026
- **Apply:** https://arc.dev/remote-jobs/j/redirect/pourtz3bec

## About the Role

* Extensive experience with ML/AI solution development across classical and generative AI.
 * Proficiency with Azure AI Foundry, Microsoft Copilot Studio, Google Vertex AI, and Snowflake Cortex AI.
 * Strong skills in integrating AI with enterprise data and systems.
 * Experience with MLOps/LLMOps automation, CI/CD, and infrastructure-as-code.
 * Ability to manage full model lifecycle and operational handoff.
 * Familiarity with experimentation, model evaluation, and deployment best practices.

## Description

The SENIOR AI ENGINEER - DATA & MLOPS role focuses on designing, building, and operationalizing machine learning and AI solutions within the IT organization. This position is responsible for developing robust AI applications such as predictive models, generative AI assistants, and intelligent automation, while ensuring their sustainability and reliability in production. The role bridges solution development with ongoing operational ownership, contributing to the enterprise's capacity to maintain and improve AI capabilities that deliver measurable business impact., * Collaborate with IT and AI teams to translate business needs into ML/AI solutions.

 * Design, develop, train, evaluate, and deploy ML and generative AI models using enterprise AI platforms.
 * Integrate AI agents with enterprise data sources and APIs.
 * Apply rigorous experimentation and evaluation to deliver accurate and effective solutions.
 * Manage the transition of AI solutions from development to production, ensuring release readiness.
 * Implement full model lifecycle management including versioning, retraining, and deprecation.
 * Define criteria for model performance and coordinate solution tuning and remediation.
 * Partner with operations engineers to develop run-books and support materials.
 * Engineer reusable MLOps/LLMOps pipelines for automated model lifecycle management.
 * Build CI/CD, testing frameworks, and infrastructure-as-code for AI solutions.
 * Standardize experimentation, tracking, and deployment using appropriate tooling.
 * Establish automated evaluation and regression testing for AI models.
 * Develop technical standards, reference patterns, and reusable frameworks.
 * Define and document processes and playbooks for AI solution development and support.
 * Mentor and upskill team members in AI development and operations.