Product Manager (AI)

National e-Governance Division; New Delhi; On-site; INR 4,000,000 - 6,000,000; Full time

Job summary

National e-Governance Division seeks a senior product leader to own the AI-driven government services roadmap and strategy. You will coordinate with AI/ML engineers, data scientists, UX designers, and policy stakeholders to deliver citizen-focused digital solutions.

You will guide discovery, define detailed specifications, and manage multiple initiatives from ideation to deployment, ensuring alignment with Digital India goals and governance standards.

Qualifications

• 10–12 years in product management with 4–6 years in AI/ML or digital transformation initiatives
• 3–5 years in senior product management leading AI-powered products from conception to launch
• 2–3 years managing cross-functional teams including data scientists, ML engineers, and business stakeholders
• Proven track record delivering enterprise-grade AI products with measurable business impact
• Prior experience in government or PSU initiatives desirable

Responsibilities

• Define and own the end-to-end product vision, strategy, and roadmap for AI-driven government services.
• Lead product discovery, market research, and user research to identify citizen pain points and opportunities for AI intervention.
• Translate complex business requirements and government use cases into detailed product specifications, user stories, and acceptance criteria for technical teams.
• Oversee complete product lifecycle from ideation through launch, adoption, and continuous improvement for 8–10 concurrent AI initiatives within the owned pod cluster.
• Manage cross-functional collaboration between AI/ML engineers, data scientists, MLOps, UX designers, business analysts, and government stakeholders.

Skills

AI/ML product management

Cross-functional leadership

Agile / SAFe

Stakeholder management

Data literacy

Education

B.Tech./B.E. in CS/IT/Data Science

MBA/M.Tech. in Management/Systems

MS/ME in AI/ML or related field

Tools

Jira

Confluence

Azure DevOps

Miro

Figma

Mixpanel/Amplitude

Job description

• B.Tech./B.E. in Computer Science, Information Technology, Data Science, or related technical field (Must have)
• MBA/M.Tech. in Management, Systems Engineering, or Product Management certification preferred
• Advanced degree (M.S./M.Tech.) in AI/ML, Computer Science, or related quantitative field highly desirable
• Certifications (Desirable): Product Management certifications (Certified Scrum Product Owner, Product School AI PM Certification), AI/ML certifications (DeepLearning.AI, Google AI Product Manager), Agile/SAFe Product Owner certifications, Programme Management certifications (PMP, PRINCE2, SAFe)

Experience

• 10–12 years in product management with minimum 4–6 years in AI/ML or digital transformation initiatives
• 3–5 years in senior product management roles leading AI-powered products from conception to launch
• 2–3 years managing cross-functional teams including data scientists, ML engineers, and business stakeholders
• Proven track record delivering enterprise-grade AI products with measurable business impact
• Prior experience in programme delivery for government or PSU initiatives desirable

Key Responsibilities

Product Ownership

• Define and own the end-to-end product vision, strategy, and roadmap for AI-driven government services within the owned pod cluster, including conversational AI, document processing, voice-enabled systems, and citizen service automation, aligned with Digital India goals and organisational objectives
• Lead product discovery, market research, and user research to identify citizen pain points and opportunities for AI intervention across government departments served by the owned pods
• Translate complex business requirements and government use cases into detailed product specifications, user stories, and acceptance criteria for technical teams
• Oversee complete product lifecycle from ideation through launch, adoption, and continuous improvement for 8–10 concurrent AI initiatives within the owned pod cluster
• Manage cross-functional collaboration between AI/ML engineers, data scientists, MLOps, UX designers, business analysts, and government stakeholders, ensuring alignment and timely delivery
• Define and track key product metrics (KPIs, OKRs) including user adoption, accuracy, response times, cost per transaction, and citizen satisfaction scores for services delivered by the owned pods
• Conduct competitive analysis of AI products in the government sector, identify best practices, and drive product differentiation strategies
• Lead go-to-market planning including rollout strategy, change management, training programmes, and adoption campaigns for government employees and citizens.
• Manage product budgets, resource allocation, vendor evaluation, and contract negotiations for AI platform components within the owned pod cluster
• Ensure Responsible AI principles are applied — ethical AI deployment, bias mitigation, transparency, and compliance with MeitY guidelines and data protection regulations
• Facilitate stakeholder reviews with government officials, executive leadership, and department heads, presenting product progress, metrics, and strategic recommendations
• Drive product innovation through experimentation with emerging AI technologies (LLMs, multimodal AI, agentic systems), evaluating feasibility for government applications
• Own the programme risk register— identify, log, track, and drive mitigation of delivery, technical, agency, and compliance risks; elevate cross-cluster or systemic risks to the AI/Solution Architect for consolidation
• Track budget consumption against the approved envelope; prepare monthly burn reports and quarterly variance analysis; ensure spend remains within allocated limits
• Define and maintain the deliverables schedule for the deliverables-linked payment component (40% of manpower cost); verify quarterly deliverables against agreed acceptance criteria before recommending payment release
• Oversee agency performance— monitor SLA compliance against the Service Level Agreement framework, track resource availability and replacement timelines, and manage agency escalations.
• Coordinate deployment mobilisation— track RDR and RFQ issuance, candidate screening, work order deployment (60-day standard, 120-day niche), and early-deployment incentive eligibility (30-day)
• Serve as the single point of escalation to NeGD leadership for all delivery, agency, or ministry issues arising within the owned allocation of work
• Ensure adherence to Confidentiality and Data Security obligations, Data Governance and Compliance provisions, and IPR obligations (AIKosh publication of reusable assets, OpenForge deposit of source code within 30 days of milestone completion) for all deliverables produced by the owned pod cluster
• AI/ML Understanding: Solid grasp of machine learning fundamentals, neural networks, LLMs, NLP, computer vision, model training, inference, evaluation metrics, and AI development lifecycle (not required to code but must understand technical trade-offs)
• Product Management: Expert proficiency in Agile, Scrum, SAFe methodologies, product discovery frameworks (Design Thinking, Jobs-to-be-Done), roadmap planning, backlog prioritisation (MoSCoW, RICE, Kano model)
• Programme Management: Working proficiency in programme governance frameworks, budget tracking, risk management, SLA monitoring, and deliverables scheduling for multi- workstream engagements
• Tools & Platforms: Advanced proficiency in Jira, Confluence, Azure DevOps, Miro, Figma, product analytics platforms (Mixpanel, Amplitude), A/B testing tools
• AI Product Lifecycle: Understanding of MLOps workflows, model deployment pipelines, monitoring for drift and performance, continuous learning systems, and production AI challenges
• Data Fluency: Ability to work with data scientists on data requirements, feature engineering, training data quality, and interpret model performance metrics (accuracy, precision, recall, F1-score)
• Technology Stack: Familiarity with cloud AI services (AWS Bedrock, Azure OpenAI, Google Vertex AI), conversational AI platforms, RAG architectures, voice processing systems, and API-based integrations