Senior Product Manager, Applied AI

Job Overview

We are looking for a Senior Product Manager, Applied AI to own customer deployments from initial discovery and product scoping through delivery, adoption, and value realization.

In this role, you will work directly with enterprise customers to understand their most important workflows, translate business objectives into clear product and technical requirements, and build a sequenced deployment roadmap. You will partner closely with Forward Deployed Engineers, internal Product and Engineering teams, and cross-functional partners across Sales, Customer Success, Data, Security, Legal, Finance, and Operations.

You will serve as the product owner for the customer engagement: accountable for the problem being solved, the scope and success criteria, the deployment roadmap, and the business value ultimately delivered. You will also help identify which customer needs represent repeatable product opportunities and work with the internal teams to incorporate those insights into the broader product roadmap.

This is an opportunity to join an emerging motion at an early stage and requires people with entrepreneurial mindset. Beyond delivering individual customer engagements, you will help define the product, operating model, playbooks, and decision frameworks required to scale this new product and motion.

What You’ll Do

Own Customer Discovery and Product Strategy

• Build trusted relationships with customer executives, business leaders, technical stakeholders, and end users.
• Lead discovery to understand customer objectives, workflows, user needs, data and technical constraints, and desired business outcomes.
• Identify and prioritize high-value use cases, define the minimum viable deployment, and establish clear success and acceptance criteria.
• Translate ambiguous customer problems into clear product requirements, prioritization decisions, and a sequenced product and deployment roadmap.

Lead End-to-End Customer Deployment

• Translate customer objectives into a clear product and deployment roadmap covering milestones, workstreams, dependencies, risks, decision points, and launch criteria.
• Create high-quality product artifacts, including problem statements, product requirements, workflow definitions, user stories, acceptance criteria, prioritization frameworks, and executive decision documents.
• Own delivery governance across Coursera and customer teams, maintaining clarity on scope, timelines, responsibilities, risks, and unresolved decisions.

Partner on Technical Solution Design and Deployment

• Work closely with Forward Deployed Engineers and internal Engineering teams to translate business workflows into deployable technical solutions.
• Participate credibly in discussions involving system architecture, APIs, data flows, integrations, identity and access management, infrastructure, security, privacy, scalability, reliability, and deployment models.
• Evaluate technical and product trade-offs, challenge assumptions, and ensure that proposed solutions are feasible, supportable, secure, and aligned with customer outcomes.
• Partner with engineering teams on AI-specific considerations such as data quality, evaluation approaches, human oversight, model limitations, responsible AI, and production-readiness criteria.

Own Business Value and Create Product Leverage

• Define the value hypothesis for each customer engagement and establish the business, adoption, and product metrics required to measure success.
• Evaluate effort, implementation complexity, cost-to-serve, and expected customer value when making scope and prioritization decisions.
• Partner with Sales, Finance, Legal, and Customer Success on scope boundaries, business cases, commercial assumptions, and material changes to customer engagements.
• Translate deployment results into clear narratives on adoption, ROI, customer impact, and future opportunities.
• Identify recurring customer workflows, requirements, and technical patterns that can become reusable product capabilities.
• Partner with Product and Engineering teams to bring field insights into the broader roadmap and build repeatable discovery, scoping, deployment, and measurement playbooks.

What We’re Looking For

• 8+ years of relevant experience across B2B product management, enterprise software, technical consulting, solutions delivery, or related roles, with clear ownership of product or customer outcomes.
• Demonstrated experience taking technically complex enterprise products, platforms, or customer deployments from discovery and scoping through production launch and adoption.
• Experience working with B2B products involving platforms, data, APIs, integrations, infrastructure, or complex enterprise workflows.
• Strong customer-facing experience working directly with senior business leaders, technical stakeholders, and executive sponsors.
• Excellent product judgment with the ability to convert ambiguous problems into clear requirements, prioritized roadmaps, and measurable outcomes.
• Strong program and delivery management skills, including managing multiple workstreams, dependencies, risks, changing scope, and executive-level communication.
• Strong technical fluency and the ability to reason about enterprise software architecture, data and integrations, cloud environments, security, privacy, and software delivery.
• Strong understanding of AI- and LLM-enabled products, including where these technologies create value, their limitations, and the considerations involved in deploying them in enterprise workflows.
• Hands-on prototyping experience with AI-assisted or low-code development tools such as Claude, Cursor, Lovable, Retool, or similar platforms.
• Financial and commercial acumen, including experience evaluating business cases, effort versus value, delivery economics, ROI, and the implications of scope and prioritization decisions.
• Ability to operate effectively in ambiguity, influence cross-functional teams without formal authority, and communicate equally well with executives, product teams, and engineers.
• Willingness to collaborate across global time zones and travel periodically for customer discovery, delivery, and executive engagements.

Nice to Have

• Experience deploying AI, machine-learning, LLM, RAG, or agentic solutions into production enterprise environments.
• Familiarity with AI evaluation, guardrails, human-in-the-loop workflows, observability, model performance, and responsible-AI practices.
• Experience working in education technology, workforce transformation, talent intelligence, skills, learning platforms, or other enterprise environments involving complex stakeholder and data ecosystems.
• Experience delivering products or programs across multiple regions, industries, or regulatory environments.