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Coursera (now merged with Udemy) is seeking an Enterprise Agentic AI Architect to design and build the core technical foundation for AI adoption across the organization. This is a hands-on architecture role reporting directly to the VP of AI and Operations, responsible for enabling multiple business pods to build and deploy AI-native solutions safely, cost-effectively, and at scale.
You will own the design and implementation of several interconnected architecture domains:
**Context & Semantic Architecture**: Design how AI agents access and understand Coursera's internal data and knowledge. Establish reusable patterns for RAG (Retrieval-Augmented Generation), 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 to ensure production-ready reliability.
**Governance & Security Architecture**: Build technical guardrails that enforce access control, data isolation, and audit logging. Design defenses specific to agentic AI, including agent identity and access control, least-privilege permissions, prompt injection defense, data leakage prevention, secure credentials, and safe tool execution.
**Self-Serve AI Development & CI/CD**: Extend Coursera's self-serve deployment path so AI-assisted, conversational development ships through automated testing, security checks, and evaluation gates—safe by default.
**LLM Cost & Performance Optimization**: Design caching, model routing, token and context optimization, and usage monitoring so AI adoption scales without linear cost growth. Establish cost, latency, quality, and usage metrics.
**Agent Operating Model & Adoption**: Define agent ownership, lifecycle management, production-readiness standards, and reusable patterns so business teams can scale AI capabilities consistently. Measure success through usage, workflow outcomes, productivity, quality, and business value.
**Enterprise Systems Integration**: Design AI solutions that understand dependencies across enterprise systems, business processes, integrations, and downstream impacts. Establish reusable patterns for APIs and integration standards like Model Context Protocol (MCP).
**Production Readiness & Operational Excellence**: Establish standards for reliability, observability, incident response, security, operational ownership, and continuous improvement across AI platforms.
You will work cross-functionally with Engineering Leadership, IT, the Data team, Security, business pods (3–4 person teams), and Change Management. This role includes direct people leadership: hiring, coaching, delegation, performance management, and developing technical leaders who can independently own architecture, delivery, stakeholder alignment, and operational outcomes.
**Requirements**
- 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 (not just individual feature delivery)
- Hands-on experience with agentic AI frameworks and orchestration (e.g., LangGraph or comparable) and familiarity with Model Context Protocol (MCP) or similar standards for connecting AI systems to enterprise data and tools
- 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—prompt caching, model routing, 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, performance development, 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
