AI Platform Architect (Teradyne, India) (Remote, India, IN)
Job Title: AI Platform Architect
Reports to: Enterprise AI Architect
Our Purpose
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 challenged 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.
What You Will Do
As an AI Platform Architect I at Teradyne , you will help shape how AI is architected, integrated, and governed across the enterprise, spanning AI agent and MCP infrastructure, platform and connector integration, AI governance and risk processes, and enterprise AI literacy. Reporting to the Enterprise AI Architect, you will translate our enterprise AI strategy into concrete architecture patterns, deployment standards, and governance practices, primarily across Azure Foundry, Microsoft Copilot Studio and Copilot Cowork, Snowflake Cortex AI, and Claude.
This is a hands-on technical, architecture-focused role. You will define reusable patterns for deploying AI agents, standardize MCP server design and CI/CD pipelines, AI platform configuration, and set up authentication and authorization middleware for AI agents and services. You will also contribute to the enterprise AI gateway and connector strategy, support AI governance and risk-review processes, and help drive enterprise AI literacy, ensuring AI initiatives scale securely, consistently, and responsibly.
Key Responsibilities
AI Platform & Environment Enablement
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Manage platform settings, integrations, and resource allocations to ensure optimal performance and cost-efficiency for development teams
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Set up and maintain authentication/authorization middleware for AI agents and services, partnering with the team that owns enterprise identity/access management
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Standardize Model Context Protocol (MCP) server design, deployment templates, and infrastructure-as-code patterns
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Design and own CI/CD pipelines for AI agent, MCP, and/or AI model deployments (GitHub Actions or Azure DevOps)
Business Outcome:
Provide well-configured, secure, and efficient AI platform environments that enable teams to build and deploy AI solutions reliably.
AI Solution Development
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Define architecture patterns and standards for deploying AI agents across the enterprise, primarily on Azure Foundry, Microsoft Copilot / Copilot Studio / Copilot Cowork, Snowflake Cortex AI, and Claude
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Establish and document reusable playbooks and modular workflows for agentic AI development, ensuring rapid adoption and consistency across teams leveraging both no-code and programmatic development environments
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Architect Retrieval-Augmented Generation (RAG) and agent infrastructure on cloud AI development platforms
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Contribute to the design and maintenance of the enterprise AI gateway, including the MCP registry, LLM traffic routing, observability, rate limiting, data redaction, and access control
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Contribute to the enterprise connector/integration strategy connecting AI tools to core business systems
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Lead architecture reviews for AI agent and enterprise copilot tooling deployments
Business Outcome:
Deliver well-architected, reusable AI solutions that accelerate adoption and demonstrate the value of enterprise AI investment.
AI Governance, Security, and Observability
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Contribute to AI governance and risk-review processes, including evaluating new LLMs before adoption and reviewing AI tool and connector requests
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Partner with security, legal, and compliance teams to help shape AI risk frameworks, policy, and data governance standards
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Contribute to enterprise AI literacy/training tracking and reporting
Business Outcome:
Strengthen AI governance and risk posture while bui