Product Manager (AI) Reports to: Director, NeGD

B.Tech./B.E. in Computer Science, Information Technology, Data Science, or related technical field (Must have)
MBA/M.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, Protected)

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

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
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
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
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; escalate cross-cluster or systemic risks to the AI/Solution Architect for consolidation
Oversee agency performance— monitor SLA compliance against the Service Level Agreement framework, track resource availability and replacement timelines, and manage agency escalations.




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
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

📌 AI/ML Product Manager (Delhi)
🏢 National e Governance Division
📍 Delhi