> Markdown version of [/jobs/ext/228117-ai-platform-architect-teradyne-north-reading](https://www.wearedevelopers.com/jobs/ext/228117-ai-platform-architect-teradyne-north-reading). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # AI Platform Architect (Teradyne, North Reading) - **Company:** Teradyne Inc. - **Location:** North Reading, MA, United States - **Experience:** Experienced - **Salary:** $77,500.0 - $124,500.0 - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, ARM Architecture, Computer Vision, Microsoft Azure, Microsoft Online Services, Information Systems, Data Security, Github, Python (Programming Language), Machine Learning, Role-Based Access Control, Software Deployment, SQL Databases, Data Logging, Data Processing, Microsoft Power Automate, Snowflake, Generative AI, AI Platforms, Kubernetes, Information Technology, Machine Learning Operations, Virtual Agents, Network Server - **Published:** May 17, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=80d37f09dfebcd34 ## About the Role Do you have experience in Technical solutions implementation?, We seek individuals who share our passion and determination. Our commitment to customer success drives us to go the extra mile. If you're ready to join us in this mission, take a closer look at the minimum criteria for the position. * 4-6 years of experience in AI/ML engineering, with hands-on expertise in enterprise AI platforms and agentic AI development. * Bachelor's degree in Computer Science, Information Systems, Engineering, or a related field. * Proficiency in Python, SQL, agentic orchestration frameworks * Hands-on experience building & deploying end-to-end AI solutions with one or more of the following AI platforms; Azure Foundry, Microsoft Copilot Studio, Vertex AI, and Snowflake Cortex AI * Experience developing and implementing multi-environment MLOps & LLMOps pipelines * Strong knowledge of AI security, observability, and governance frameworks * Proven 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. ## Description As an AI Platform Architect I at Teradyne, you will be a hands-on builder and a critical enabler of our enterprise AI strategy. Your primary mission is to construct, configure, and maintain the environments and tools that our organization will use to create and deploy cutting-edge AI solutions. Reporting to the Enterprise AI Architect, you will be responsible for translating the enterprise AI Strategy into functional, secure, and observable enablers for Teradyne systems. This is a deeply technical role focused on implementation. You will be responsible for setting the direction and partnering with infrastructure and engineering teams for everything from setting up secure, role-based environments in Microsoft Copilot Studio, Azure AI Foundry, and Google Vertex AI, to building AI agents for real-world use cases. You will develop and maintain RAG workflows, MCP Servers, and use Microsoft 365 services like Power Automate to streamline business processes. Your work is the essential foundation that empowers our teams to innovate and execute on our AI vision. AI Platform & Environment Enablement * Create, configure, and manage the development and deployment environments for our core AI platforms: Microsoft Copilot Studio, Azure AI Foundry, Google Vertex AI, and Snowflake Cortex AI. * Implement and maintain robust Role-Based Access Controls (RBAC) and other critical security configurations within each platform to ensure a "secure by default" posture for all users and services. * Manage platform settings, integrations, and resource allocations to ensure optimal performance and cost-efficiency for development teams. * Engineer reusable MLOps/LLMOps pipelines using Azure DevOps or GitHub Actions to automate the complete lifecycle of AI models, from testing to production deployment. * Mentor and upskill teams in agentic AI design, MLOps & LLMOps practices, and solution architecture, fostering a culture of innovation and continuous learning. AI Solution Development * Establish and document reusable patterns, playbooks, and modular workflows for agentic AI, ensuring rapid adoption and consistency across teams leveraging both no-code and programmatic development environments, such as Microsoft Copilot and Azure Foundry. * Integrate AI agents with enterprise data sources, APIs, and MCP servers for interoperability and future-proofing. * Build and maintain sophisticated Retrieval-Augmented Generation (RAG) workflows, creating vector databases and embedding pipelines to ground models in proprietary enterprise data. * Architect and develop the centralized MCP server and gateway, ensuring secure, observable, and governed interactions for all enterprise AI agents. * Build automated workflows using Power Automate and M365, integrated with AI agents and enterprise systems to streamline business processes and enhance productivity. AI Governance, Security, and Observability * Implement technical guardrails across all AI platforms to enforce data handling, model usage, and responsible AI principles. * Apply security controls to AI agents and RAG workflows, mitigating risks like prompt injection and unauthorized data access. * Engineer and maintain a centralized logging solution to create detailed audit trails of all AI agent interactions and decisions. * Implement monitoring dashboards to track AI platform health, agent performance, resource utilization, and operational costs. ## Related Videos - [AI-Augmented DevOps with Platform Engineering](https://www.wearedevelopers.com/videos/1614-ai-augmented-devops-with-platform-engineering) - [Innovating Developer Tools with AI: Insights from GitHub Next](https://www.wearedevelopers.com/videos/1268-innovating-developer-tools-with-ai-insights-from-github-next) - [How Cisco embraced a DevOps culture within its network engineering team](https://www.wearedevelopers.com/videos/99-how-cisco-embraced-a-devops-culture-within-its-network-engineering-team) - [Understanding Kubernetes in a visual way](https://www.wearedevelopers.com/videos/100085-understanding-kubernetes-in-a-visual-way) - [Blueprints for Success: Steering a Global Data & AI Architecture](https://www.wearedevelopers.com/videos/1577-blueprints-for-success-steering-a-global-data-ai-architecture) - [Instant KAI Sandboxes with vCluster: Multi-Tenant, Multi-Scheduler GPU Sharing](https://www.wearedevelopers.com/videos/100333-instant-kai-sandboxes-with-vcluster-multi-tenant-multi-scheduler-gpu-sharing) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift) - [Stephan Gillich - Bringing AI Everywhere](https://www.wearedevelopers.com/magazine/489-stephan-gillich-bringing-ai-everywhere) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [Got AI ideas but no money? Here are 10 free ways to level up your AI skills with Google Cloud](https://www.wearedevelopers.com/magazine/600-got-ai-ideas-but-no-money-here-are-10-free-ways-to-level-up-your-ai-skills-with-google-cloud) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production)