AI Platform Architect (Teradyne, India) (Remote, India, IN)
Teradyne
IndiaremotePosted 8 days ago
Skill Required
AI-Platform-ArchitectAI-Platform-EngineeringEnterprise-AI-ArchitectureAI-Solutions-ArchitectureAI-InfrastructureAI-Platform-EngineerAI-ML-Platform-EngineerPlatform-AI-EngineerML-Platform-ArchitectPlatform-ArchitectAIAzureGitHub Actionsrelated fieldObservabilityCD pipelinesEngineeringanalyticalSnowflakedesigningsecuritybuildingHardhatPythondesignDevOpsGitCI/CDCloudGenerative AIRAGSQLandCICDFulltime
Key highlights
- Required experience: 2–5 years in AI/ML or platform engineering
- Primary platforms: Azure Foundry, Microsoft Copilot, Snowflake Cortex AI, Claude
- Role type: Hands-on technical, architecture-focused
- Reports to: Enterprise AI Architect
Role overview
As an AI Platform Architect I at Teradyne, you will shape how AI is architected, integrated, and governed across the enterprise. You will translate enterprise AI strategy into concrete architecture patterns, deployment standards, and governance practices across key platforms such as Azure Foundry, Microsoft Copilot Studio, Snowflake Cortex AI, and Claude.
Responsibilities
- Manage platform settings, integrations, and resource allocations to ensure optimal performance and cost-efficiency for development teams.
- Set up and maintain authentication/authorization middleware for AI agents and services, partnering with the team that owns enterprise identity/access management.
- Standardize Model Context Protocol (MCP) server design, deployment templates, and infrastructure-as-code patterns.
- Design and own CI/CD pipelines for AI agent, MCP, and/or AI model deployments (GitHub Actions or Azure DevOps).
- Define architecture patterns and standards for deploying AI agents across the enterprise.
- Establish and document reusable playbooks and modular workflows for agentic AI development.
- Architect Retrieval-Augmented Generation (RAG) and agent infrastructure on cloud AI development platforms.
- 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.
- Contribute to the enterprise connector/integration strategy connecting AI tools to core business systems.
- Lead architecture reviews for AI agent and enterprise copilot tooling deployments.
- Contribute to AI governance and risk-review processes, including evaluating new LLMs before adoption and reviewing AI tool and connector requests.
- Partner with security, legal, and compliance teams to help shape AI risk frameworks, policy, and data governance standards.
- Contribute to enterprise AI literacy/training tracking and reporting.
Requirements
- 2–5 years of experience in AI/ML or platform engineering, with hands-on experience deploying AI systems in an enterprise environment.
- Bachelor's degree in Computer Science, Information Systems, Engineering, or a related field.
- Hands-on experience with Microsoft Azure AI Foundry.
- Proficiency in Python and SQL.
- Hands-on experience building and deploying AI Agents and MCP Servers.
- Experience designing CI/CD pipelines using GitHub Actions or Azure DevOps.
- Experience with Infrastructure as Code.
- Hands-on experience setting up authentication/authorization.
- Experience building custom RAG pipelines.
- Familiarity with AI governance/risk frameworks.
- Understanding of API/connector integration patterns for enterprise systems.
- 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.
- Comfortable contributing to governance and security review processes.
Nice to have
- Additional experience with Microsoft Copilot Studio, Copilot Cowork, Snowflake Cortex AI, and Claude.
Additional details
- Reports to: Enterprise AI Architect.
- Business Outcome: Provide well-configured, secure, and efficient AI platform environments that enable teams to build and deploy AI solutions reliably.
- Business Outcome: Deliver well-architected, reusable AI solutions that accelerate adoption and demonstrate the value of enterprise AI investment.
- Business Outcome: Strengthen AI governance and risk posture while building enterprise-wide AI literacy and capability.
- Originally posted on Himalayas.