Enterprise Agentic AI Architect

About Coursera + Udemy Coursera and Udemy are now one company, bringing together two mission-driven brands to create the world’s most powerful platform for turning learning into progress. Together, we help more than 300 million learners and 12,000+ enterprise customers build the skills they need for a world being reshaped by AI. Read more about the combined company by visiting our blog. Why join us now? AI is transforming how people learn, work, and grow, and the need for new skills has never been greater. Coursera brings trusted content and credentials from leading university and industry partners, while Udemy brings a dynamic skills marketplace and global network of real-world experts. By combining these strengths, we can connect more people and organizations to the skills they need, when they need them. Shape what comes next By joining our team, you’ll have the opportunity to reshape how the world learns and applies skills—and help millions of people participate in the new economy. Bring your ideas, expertise, and perspective to meaningful work that can make a difference at global scale. About the Role Coursera’s AI adoption has moved fast — engineering teams code AI-first, dozens of internal tools have shipped, and functions across the business are building their own AI agents and automations. The next phase requires something different: a shared technical foundation that lets many teams build safely and cost-effectively at scale, instead of each team solving governance, security, and cost from scratch. This role owns that foundation. The Senior Agentic AI Architect Lead designs and builds the core architecture that Coursera’s AI Builders and business pods will run on — so that a small team (a Business Product Owner, a Process Expert, an IT Product Owner, and an AI Engineer) can redesign a workflow and ship an AI-native solution in weeks, not months. This is a hands-on architecture role, not a pure strategy role: you will be building, not just advising. What You’ll Own • Context & Semantic Architecture - Design how AI agents access and understand Coursera’s internal data and knowledge, so agents retrieve the right information rather than guessing or over-fetching. Establish reusable patterns for RAG, semantic search, structured data access, and enterprise context, partnering with the Data team on existing data models and pipelines. • Agentic Architecture & Harness Engineering - Design reusable patterns for agent orchestration, tool use, multi-step workflows, state management, and human-in-the-loop decisions. Build the engineering harness around agents, including testing, evaluation, guardrails, retries, observability, and failure recovery, so agentic solutions are reliable and production-ready. • Governance Architecture - Build technical guardrails that enforce—not just document—who can build what, what data and systems an agent can access, and what gets logged for audit and review. Establish standards for agent ownership, lifecycle management, and production readiness. • Security Architecture - Design defenses specific to agentic AI, including agent identity and access control, least-privilege permissions, prompt injection, data leakage, secure credentials, and safe tool execution. Coordinate with Security to extend existing enterprise security standards. • Self-Serve AI Development (“Vibe Coding”) with CI/CD Automation - Extend and mature Coursera’s self-serve deployment path so AI-assisted, conversational development ships through automated testing, security checks, and evaluation gates—safe by default, not safe by manual gatekeeping. • LLM Cost & Performance Optimization - Design caching, model routing (matching task complexity to the right-sized model), token and context optimization, and usage monitoring so AI adoption can scale without costs growing linearly. Establish cost, latency, quality, and usage metrics. • Agent Operating Model & Adoption - Define agent ownership, lifecycle management, production-readiness standards, adoption metrics, and reusable patterns so business teams can scale AI capabilities consistently across functions. Measure success through usage, workflow outcomes, productivity, quality, and business value. • Architecture Strategy & Execution - Turn ambiguous business problems into scalable technical direction, practical roadmaps, architecture decisions, and production delivery. • Enterprise Systems & Integration Architecture - Design AI solutions that understand dependencies across enterprise systems, business processes, integrations, and downstream impacts. Establish reusable patterns for APIs, enterprise tools, and Model Context Protocol (MCP) or comparable integration standards where appropriate. • Production Readiness & Operational Excellence - Establish standards for reliability, observability, incident response, security, operational ownership, and continuous improvement across AI platforms and solutions. How This Role Works With Others This role sits at the intersection of four groups and requires strong cross-functional partnership—it does not operate in isolation. • Engineering Leadership: Partner directly with Engineering leadership to align architecture decisions and share resources and infrastructure where possible, avoiding duplicate build efforts across the company. • IT: IT owns broader company-wide tooling and platform decisions (procurement, provisioning, cost governance, enterprise policy). Coordinate closely with IT so agentic architecture decisions align with company-wide technical direction rather than creating a parallel, ungoverned stack. • Data Team: The Data team owns Coursera’s underlying data strategy. Partner with them to ensure context and semantic architecture aligns with existing data models, pipelines, and ownership rather than building a parallel, disconnected data layer for AI. • Security Team: Design security architecture specific to agentic AI—including access control, prompt injection defense,