Enterprise Agentic AI Architect

India · 12+ years experience · Posted Oct 14, 2026

At a glance

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.

Coursera is seeking an Enterprise Agentic AI Architect to own the core architecture that powers AI Builders and business pods across the company.
This is a hands-on architecture and leadership role focused on building scalable, secure, and cost-effective AI solutions rather than pure strategy.
The position requires establishing reusable patterns for agent orchestration, semantic architecture, and self-serve AI development to scale adoption safely.

Design how AI agents access and understand internal data and knowledge using 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.
Build the engineering harness around agents, including testing, evaluation, guardrails, retries, observability, and failure recovery.
Build technical guardrails that enforce who can build what, system access permissions, and audit logs.
Design security 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.
Extend and mature self-serve deployment paths so AI-assisted, conversational development ships through automated testing, security checks, and evaluation gates.
Design caching, model routing, token and context optimization, and usage monitoring to control LLM costs and performance.
Define agent ownership, lifecycle management, production-readiness standards, adoption metrics, and reusable patterns for business teams.
Turn ambiguous business problems into scalable technical direction, practical roadmaps, architecture decisions, and production delivery.
Build, coach, and develop the architecture and engineering capability required to scale the platform, including hiring, technical leadership development, and performance management.

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.
Knowledge of AI security fundamentals, including access control, data isolation, prompt injection defense, and secure tool execution.
Experience with CI/CD, cloud infrastructure, APIs, and production operations.
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.