Enterprise Agentic AI Architect

Coursera's AI adoption has moved fast, and the next phase requires a shared technical foundation that lets many teams build safely and cost-effectively at scale. This hands-on architecture role owns that foundation, designing and building the core architecture that Coursera's AI Builders and business pods will run on. The role also includes people leadership responsibilities and reports directly to the VP, AI and Operations.

Responsibilities

Design how AI agents access and understand internal data and knowledge, establishing reusable patterns for RAG, semantic search, structured data access, and enterprise context; Design reusable patterns for agent orchestration, tool use, multi-step workflows, state management, and human-in-the-loop decisions, and build the engineering harness around agents (testing, evaluation, guardrails, retries, observability, failure recovery); Build technical guardrails that enforce who can build what, what data and systems an agent can access, and what gets logged for audit; Design agentic AI security defenses (agent identity, least-privilege permissions, prompt injection, data leakage, secure credentials, safe tool execution) in coordination with Security; Extend and mature the self-serve AI development and CI/CD path with automated testing, security checks, and evaluation gates; Design caching, model routing, token and context optimization, and usage monitoring for LLM cost and performance; Define agent ownership, lifecycle management, production-readiness standards, and adoption metrics; Turn ambiguous business problems into scalable technical direction, roadmaps, architecture decisions, and production delivery; Design AI solutions that understand dependencies across enterprise systems and establish reusable patterns for APIs, enterprise tools, and Model Context Protocol (MCP) or comparable standards; Establish standards for reliability, observability, incident response, security, and operational ownership across AI platforms; Build, coach, and develop the architecture and engineering capability required to scale the platform, including hiring and performance management; Report directly to the VP, AI and Operations with regular updates on architecture progress, risks, business impact, and resourcing needs

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; 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 (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