Product Manager AI

ROLE OVERVIEW
The Product Manager – AI will own the product vision, roadmap, lifecycle, adoption, governance, and programme delivery for a portfolio of government AI services. The role combines AI product management with cross-functional leadership, stakeholder management, budget oversight, agency governance, risk management, and compliance.

Educational Qualifications

- B.Tech./B.E. in Computer Science, Information Technology, Data Science, or a related technical field is required.
- MBA, M.Tech., Systems Engineering degree, or recognised product-management qualification is preferred.
- An advanced degree in AI/ML, Computer Science, or a related quantitative field is highly desirable.
- Desirable certifications include CSPO, AI product-management credentials, Agile/SAFe Product Owner, PMP, PRINCE2, or SAFe.

Experience

- 10–12 years in product management, including 4–6 years in AI/ML products or digital-transformation initiatives.
- 3–5 years in senior product roles leading AI-powered products from discovery to launch and scale.
- 2–3 years managing cross-functional teams comprising data scientists, ML engineers, designers, analysts, and business stakeholders.
- Demonstrated delivery of enterprise-grade AI products with measurable user, operational, or financial impact.
- Government or PSU programme-delivery experience is desirable.

Key Responsibilities

- Define and own the product vision, strategy, roadmap, positioning, and outcomes for AI-driven government services across the assigned pod cluster.
- Lead product discovery, market research, user research, and problem validation to identify citizen and departmental needs suitable for AI intervention.
- Translate complex government use cases into product requirements, user journeys, specifications, user stories, acceptance criteria, and measurable success metrics.
- Oversee the full lifecycle of 8–10 concurrent AI initiatives, from ideation and prioritisation through build, launch, adoption,



measurement, and continuous improvement.
- Coordinate AI/ML engineers, data scientists, MLOps, UX, business analysis, QA, agencies, and government stakeholders to maintain alignment and delivery discipline.
- Define and track KPIs and OKRs such as adoption, model quality, response time, cost per transaction, service completion, and citizen satisfaction.
- Lead competitive analysis, experimentation, roadmap prioritisation, and evaluation of emerging technologies including LLMs, multimodal AI, and agentic systems.
- Own rollout strategy, change management, user training, communications, and adoption programmes for government employees and citizens.
- Manage product budgets, resource allocation, vendor evaluation, commercial inputs, and platform-component decisions.
- Ensure Responsible AI, bias mitigation, transparency, privacy, security, accessibility, and regulatory compliance are embedded in product design and delivery.
- Facilitate executive and government stakeholder reviews, presenting progress, evidence, trade-offs, risks, and recommendations.
- Own the programme risk register and drive mitigation of delivery, technical, agency, financial, and compliance risks; escalate systemic risks appropriately.
- Track budget consumption, produce monthly burn reports and quarterly variance analysis, and maintain spend within approved limits.
- Maintain deliverables schedules and verify acceptance evidence for deliverables-linked payments before recommending release.
- Monitor agency performance, SLA compliance, resource availability, replacement timelines, and escalations.




- Coordinate deployment mobilisation including RDR/RFQ issuance, candidate screening, work-order deployment, standard and niche hiring timelines, and early-deployment incentives.
- Act as the primary escalation point to NeGD leadership for delivery, agency, and ministry issues within the assigned portfolio.
- Ensure compliance with confidentiality, data security, data governance, IPR, AIKosh publication, and OpenForge source-code deposit obligations.

Technical Competencies

- AI/ML: machine-learning fundamentals, neural networks, LLMs, NLP, computer vision, training, inference, evaluation metrics, and production trade-offs.
- Product Management: Agile, Scrum, Protected, Design Thinking, Jobs-to-be-Done, roadmap planning, and prioritisation frameworks such as MoSCoW, RICE, and Kano.
- Programme Management: governance, budget tracking, risk management, SLA monitoring, deliverables scheduling, and multi-workstream coordination.
- Tools: Jira, Confluence, Azure DevOps, Miro, Figma, Mixpanel, Amplitude, and A/B testing tools.
- AI Product Lifecycle: MLOps, deployment pipelines, drift and performance monitoring, continuous learning, model governance, and production operations.
- Data Fluency: training-data quality, feature engineering, experimentation, SQL, Excel, Tableau, Power BI, and interpretation of accuracy, precision, recall, and F1-score.
- Technology: AWS Bedrock, Azure OpenAI, Google Vertex AI, conversational AI, RAG, voice systems, multimodal AI, agents, and API integrations.
- Security and Compliance: data classification, access control, information assurance, GDPR, DPDPA 2023, and MeitY Responsible AI guidelines.
- Government Delivery: public procurement, RDR/RFQ processes, multi-agency coordination, and MeitY/NeGD delivery frameworks.
- Communication: executive presentation, stakeholder negotiation, facilitation, and translation of technical concepts for non-technical audiences.

📌 Product Manager (Delhi)
🏢 Expertshub.ai
📍 Delhi