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.

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.

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 a shared technical foundation that lets many teams build safely and cost-effectively at scale.

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 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. Establish reusable patterns for RAG, semantic search, structured data access, and enterprise context, partnering with the Data team.
• 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.
• Governance Architecture: Build technical guardrails that enforce 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 development ships through automated testing, security checks, and evaluation gates.
• LLM Cost & Performance Optimization: Design caching, model routing, 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.
• 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.
• 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.

• Engineering Leadership: Align architecture decisions and share resources and infrastructure where possible, avoiding duplicate build efforts.
• IT: Coordinate closely so agentic architecture decisions align with company-wide technical direction.
• Data Team: Ensure context and semantic architecture aligns with existing data models, pipelines, and ownership.
• Security Team: Design agentic AI security architecture, including access control, prompt injection defense, and credential handling.
• Business Pods: Enable 3–4 person pods to redesign workflows, build AI-native solutions, and drive measurable business outcomes.
• Change Management & Enablement: Help teams adopt AI-enabled workflows through enablement, feedback loops, and process redesign.

People Leadership

Build, coach, and develop the architecture and engineering capability required to scale the platform. This includes hiring, delegation, technical leadership development, performance management, and creating clear ownership across teams.

Report directly to the VP, AI and Operations, providing regular updates on architecture progress, risks, business impact, and resourcing needs.

What We’re Looking For

Must-Have

• 12+ years of experience with clear progression from engineer to senior/staff engineer to architect leader, including ownership of platforms or infrastructure used by multiple teams.
• Hands-on experience with agentic AI frameworks and orchestration (e.g., LangGraph or comparable) and familiarity with Model Context Protocol (MCP) or similar standards.
• Experience designing agent harnesses, evaluation frameworks, guardrails, testing, observability, and failure recovery for production AI systems.
• Practical experience with enterprise context architecture, RAG, semantic search, or structured data access.
• Practical experience with LLM cost and performance engineering, including prompt caching, model routing, and token economics, with quantifiable results.
• AI security fundamentals, including access control, data isolation, prompt injection defense, and secure tool execution.
• CI/CD, cloud infrastructure, APIs, and production operations experience.
• Experience defining enterprise AI architecture, reusable platform patterns, operating models, or governance standards across multiple teams.
• Direct people-management experience, including hiring, coaching, delegation, and building technical leadership capacity.
• Experience measuring AI adoption, productivity, quality, reliability, cost, and business value.
• Hands-on experience taking architecture from design through implementation, deployment, observability, and production operations.
• Strong architecture decision-making and communication skills, with the ability to align technical and business stakeholders around trade-offs involving value, risk, cost, and execution.