Enterprise Agentic AI Architect

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.

• Context & Semantic Architecture - Design how AI agents access and understand Coursera’s internal data and knowledge, so agents retrieve the right information rather than guessing or over-fetching. Establish reusable patterns for RAG, 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, so agentic solutions are reliable and production-ready.
• Governance Architecture - Build technical guardrails that enforce—not just document—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, conversational development ships through automated testing, security checks, and evaluation gates—safe by default, not safe by manual gatekeeping.
• LLM Cost & Performance Optimization - Design caching, model routing (matching task complexity to the right-sized model), 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 across functions. Measure success through usage, workflow outcomes, productivity, quality, and business value.
• 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 where appropriate.
• Production Readiness & Operational Excellence - Establish standards for reliability, observability, incident response, security, operational ownership, and continuous improvement across AI platforms and solutions.

This role sits at the intersection of four groups and requires strong cross-functional partnership—it does not operate in isolation.

• Engineering Leadership: Partner directly with Engineering leadership to align architecture decisions and share resources and infrastructure where possible, avoiding duplicate build efforts across the company.
• IT: IT owns broader company-wide tooling and platform decisions (procurement, provisioning, cost governance, enterprise policy). Coordinate closely with IT so agentic architecture decisions align with company-wide technical direction rather than creating a parallel, ungoverned stack.
• Data Team: The Data team owns Coursera’s underlying data strategy. Partner with them to ensure context and semantic architecture aligns with existing data models, pipelines, and ownership rather than building a parallel, disconnected data layer for AI.
• Security Team: Design security architecture specific to agentic AI—including access control, prompt injection defense, and credential handling—in coordination with the Security team, which owns broader security standards and policy.
• Business Pods: Enable the 3–4 person pods (Business Product Owner, Process Expert, IT Product Owner, AI Engineer) to redesign workflows, build AI-native solutions, and drive measurable business outcomes using reusable architecture.
• Change Management & Enablement: Help teams adopt AI-enabled workflows through enablement, feedback loops, process redesign, and continuous improvement.

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.

Build strong technical leaders who can independently own architecture, delivery, stakeholder alignment, and operational outcomes.

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

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—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.