[Hiring] Enterprise Agentic AI Architect @Cousera Internal Rehire Job Board

Role Description

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.
• Agentic Architecture & Harness Engineering: Design reusable patterns for agent orchestration, tool use, multi-step workflows, state management, and human-in-the-loop decisions.
• 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.
• Security Architecture: Design defenses specific to agentic AI, including agent identity and access control, least-privilege permissions, and secure credentials.
• Self-Serve AI Development (“Vibe Coding”) with CI/CD Automation: Extend and mature Coursera’s self-serve deployment path.
• LLM Cost & Performance Optimization: Design caching, model routing, token and context optimization, and usage monitoring.
• Agent Operating Model & Adoption: Define agent ownership, lifecycle management, production-readiness standards, and adoption metrics.
• Architecture Strategy & Execution: Turn ambiguous business problems into scalable technical direction.
• Enterprise Systems & Integration Architecture: Design AI solutions that understand dependencies across enterprise systems.
• Production Readiness & Operational Excellence: Establish standards for reliability, observability, incident response, and continuous improvement.

How This Role Works With Others

• Engineering Leadership: Partner directly with Engineering leadership to align architecture decisions.
• IT: Coordinate closely with IT so agentic architecture decisions align with company-wide technical direction.
• Data Team: Partner with the Data team to ensure context and semantic architecture aligns with existing data models.
• Security Team: Design security architecture specific to agentic AI in coordination with the Security team.
• Business Pods: Enable the pods to redesign workflows, build AI-native solutions, and drive measurable business outcomes.
• Change Management & Enablement: Help teams adopt AI-enabled workflows through enablement and continuous improvement.

People Leadership

• Build, coach, and develop the architecture and engineering capability required to scale the platform.
• Build strong technical leaders who can independently own architecture, delivery, stakeholder alignment, and operational outcomes.

What We’re Looking For

• 12+ years of experience with clear progression from engineer to senior/staff engineer to architect leader.
• Hands-on experience with agentic AI frameworks and orchestration.
• 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.
• AI security fundamentals, including access control and secure tool execution.
• CI/CD, cloud infrastructure, APIs, and production operations experience.
• Experience defining enterprise AI architecture and reusable platform patterns.
• Direct people-management experience, including hiring and coaching.
• Experience measuring AI adoption, productivity, quality, reliability, cost, and business value.
• Hands-on experience taking architecture from design through implementation and production operations.
• Strong architecture decision-making and communication skills.