New opportunity

Senior Product Manager, Applied AI

Coursera · Not Available, India

About this role

The Senior Product Manager, Applied AI will own customer deployments from initial discovery and product scoping through delivery, adoption, and value realization. This role involves working directly with enterprise customers to translate business objectives into clear product and technical requirements, while building a sequenced deployment roadmap to scale AI solutions.

Responsibilities

Build trusted relationships with customer executives, business leaders, and technical stakeholders.
Lead discovery to understand customer objectives, workflows, data constraints, and desired business outcomes.
Translate ambiguous customer problems into clear product requirements, prioritization decisions, and deployment roadmaps.
Own delivery governance, maintaining clarity on scope, timelines, dependencies, and risks.
Partner with Forward Deployed Engineers and internal teams to translate workflows into deployable technical solutions.
Evaluate technical trade-offs regarding system architecture, APIs, security, privacy, and scalability.
Define value hypotheses and establish metrics to measure success, adoption, and ROI.
Identify recurring customer requirements to incorporate into the broader product roadmap and build reusable playbooks.

Required qualifications

8 years of relevant experience across B2B product management, enterprise software, technical consulting, or solutions delivery.
Demonstrated experience taking complex enterprise products or deployments from discovery through production launch.
Experience working with platforms, data, APIs, integrations, and complex enterprise workflows.
Strong customer-facing experience with senior business leaders and technical stakeholders.
Strong technical fluency in enterprise software architecture, cloud environments, and security.
Understanding of AI- and LLM-enabled products, including their value, limitations, and deployment considerations.
Hands-on prototyping experience with AI-assisted or low-code tools such as Claude, Cursor, Lovable, or Retool.
Financial and commercial acumen regarding business cases, delivery economics, and ROI.

Preferred qualifications

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, and responsible-AI practices.
Experience in education technology, workforce transformation, or talent intelligence.
Experience delivering products across multiple regions or regulatory environments.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

B2B Product ManagementEnterprise SoftwareTechnical ConsultingSolutions DeliveryProduct StrategyCustomer DiscoveryRoadmap PlanningSystem ArchitectureAPI IntegrationData FlowsIdentity and Access ManagementCloud EnvironmentsSecurityPrivacyAI-enabled ProductsLLMPrototypingClaudeCursorLovableRetoolFinancial AcumenBusiness Case EvaluationProgram Management

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

Customer-to-Product Feedback Loop
The role requires identifying recurring customer requirements during deployments and feeding these insights back into the core product roadmap. You must demonstrate how you translate field-level learnings into reusable playbooks and platform capabilities to scale AI solutions beyond individual engagements.
End-to-End Deployment Governance
You are responsible for the entire lifecycle from discovery to production launch. This requires managing complex dependencies, cross-functional stakeholders, and delivery timelines while maintaining clear documentation on scope, risks, and success metrics to ensure measurable ROI for enterprise clients.
Technical Solution Design and Feasibility
You must partner with Forward Deployed Engineers to translate business workflows into technical requirements. This involves evaluating trade-offs in system architecture, data integration, security, and AI-specific constraints like model limitations and responsible-AI practices.

Likely challenges to think through

Navigating Early-Stage Ambiguity
As this is an emerging motion, you will face significant ambiguity in operating models and decision frameworks. Prepare by highlighting your experience building processes from scratch and your ability to influence cross-functional teams without formal authority to establish new standards.
Balancing Customization vs. Scalability
A core tension exists between meeting specific enterprise customer needs and building a scalable, reusable product. Prepare to discuss how you prioritize high-value use cases while resisting 'feature creep' that could hinder the long-term product roadmap.
Managing AI-Specific Deployment Risks
Deploying AI solutions involves unique challenges such as data quality, observability, and human-in-the-loop requirements. Prepare to discuss how you mitigate these risks while managing executive expectations regarding the limitations and performance of LLM-enabled products.

Practice your answers

Technical practice

Translating Ambiguous AI Requirements

What to coverDescribe your process for taking an ambiguous enterprise business problem and refining it into a concrete, technical product roadmap. Focus on how you balance customer objectives with technical feasibility, data constraints, and the limitations of current LLM or AI models.

AI Deployment and Production Readiness

What to coverWhat criteria do you use to determine if an AI-enabled solution is ready for production? Discuss your approach to managing AI-specific risks such as data quality, model hallucinations, guardrails, and the necessity of human-in-the-loop workflows.

System Architecture and Integration

What to coverHow do you evaluate the architectural trade-offs when integrating AI solutions into existing enterprise environments? Address considerations regarding API design, data privacy, security, and scalability when connecting to legacy systems.

Prototyping with Low-Code Tools

What to coverProvide an example of how you have used low-code or AI-assisted tools like Retool, Cursor, or Claude to accelerate the discovery or prototyping phase. Explain how these tools help in validating a value hypothesis before committing to full-scale engineering resources.

Defining Success Metrics and ROI

What to coverHow do you establish success metrics for an AI deployment that goes beyond simple technical performance? Explain how you align adoption metrics and ROI calculations with the specific business outcomes expected by enterprise stakeholders.

Managing Technical Debt and Reusability

What to coverAs a product manager, how do you identify recurring customer requirements that should be abstracted into reusable product playbooks? Discuss the balance between building custom solutions for a specific client and contributing to the core product roadmap.

Data Privacy and Security in AI

What to coverWhat is your framework for addressing security and privacy concerns when deploying AI solutions in a regulated enterprise environment? Suggestion: Focus on how you communicate these constraints to non-technical stakeholders while ensuring compliance.

Evaluating AI Model Performance

What to coverHow do you approach AI evaluation and observability in a production environment? Discuss the methods you use to monitor model performance over time and how you handle drift or unexpected output behavior.

Behavioural practice

Managing Stakeholder Expectations

What to coverDescribe a situation where you had to manage conflicting priorities between a customer's business goals and technical limitations. How did you maintain the relationship while ensuring the project remained within scope and feasible?

Influencing Without Authority

What to coverGive an example of how you influenced cross-functional teams—such as Engineering, Legal, or Finance—to align on a complex deployment strategy. Focus on how you used data and clear communication to build consensus.

Navigating Project Risks

What to coverTell me about a time a deployment faced significant risks or scope creep. How did you identify the issue, communicate it to executive stakeholders, and adjust the roadmap to ensure successful delivery?

Operating in Ambiguity

What to coverDescribe a project where you were tasked with building a new motion or process from the ground up. How did you define the operating model and ensure that your team remained focused on measurable impact?

Sharpen your knowledge

Topics to prepare

Enterprise AI Deployment Lifecycle
Review the end-to-end process of moving AI solutions from discovery to production. Focus on defining success metrics, managing technical dependencies, and establishing governance. This is relevant because the role requires owning the full customer deployment lifecycle and ensuring measurable ROI.
AI Product Strategy and Evaluation
Study frameworks for evaluating AI model performance, including RAG (Retrieval-Augmented Generation), guardrails, and human-in-the-loop workflows. This is relevant as the role involves assessing technical trade-offs and ensuring production-readiness for enterprise clients.

Skills to revise

AI Prototyping with Low-Code Tools
Practice building functional prototypes using tools like Retool, Cursor, or Claude to demonstrate rapid solutioning. This is a specific requirement for the role to translate customer workflows into tangible technical solutions.
Technical Requirements Translation
Practice converting ambiguous business objectives into structured product requirements, user stories, and technical specifications. This is essential for bridging the gap between enterprise customer needs and engineering execution.
Commercial and Financial Acumen
Develop exercises to calculate ROI, cost-to-serve, and business case justification for enterprise software deployments. This is required to make informed prioritization decisions and manage delivery economics.

Your preparation checklist

Prepare an enterprise AI deployment case study
Develop a structured walkthrough of a past project where you took an AI or complex software solution from discovery to production. Focus on how you managed technical constraints, stakeholder expectations, and ROI metrics, specifically highlighting your role in bridging the gap between business objectives and engineering execution.
Demonstrate hands-on prototyping proficiency
Prepare a brief demonstration or portfolio piece using low-code or AI-assisted tools like Retool, Cursor, or Claude. Show how you used these tools to rapidly prototype a workflow or solve a specific technical hurdle, demonstrating your ability to move from concept to functional solution quickly.
Formulate a framework for AI deployment governance
Create a sample 'deployment playbook' outline that addresses how you would manage risks, security, privacy, and human-in-the-loop requirements for an enterprise AI implementation. This should reflect your understanding of balancing innovation with the rigorous standards required by large-scale enterprise customers.
Prepare strategic questions for the hiring team
Draft thoughtful questions regarding Coursera's current AI product roadmap, how the team balances custom customer deployments with core product development, and the specific metrics used to define success for their 'Applied AI' engagements.
Refine your narrative on scaling repeatable solutions
Prepare to discuss your experience in identifying recurring customer needs and turning them into reusable product features. Be ready to provide concrete examples of how you have successfully influenced a broader product roadmap based on insights gathered from individual customer deployments.

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