AI Product Manager

Corover; Delhi; Hybrid; INR 2,000,000 - 4,000,000; Full time

Job summary

Corover in India is seeking an experienced AI Product Leader to define and own the end-to-end product vision, strategy, and roadmap for AI-driven government services including conversational AI, document processing, voice-enabled systems, and citizen service automation aligned with Digital India goals.

You will lead product discovery, translate government use cases into detailed specifications, manage eight to ten concurrent AI initiatives, and coordinate between AI/ML engineers, data

Qualifications

• Strong AI/ML understanding of lifecycle, tradeoffs, and evaluation metrics.
• Expertise in Agile, Scrum, SAFe and product discovery frameworks.
• Experience with Jira/Confluence and product analytics tools.

Responsibilities

• Define and own the end-to-end product vision, strategy, and roadmap for AI-driven government services.
• Lead product discovery, market and user research to identify citizen pain points for AI interventions.
• Translate complex government use cases into detailed specs, user stories, and acceptance criteria.

Skills

AI/ML understanding

Product management

Data fluency

Security & compliance

Analytics & metrics

Communication

Tools

Jira

Confluence

Azure DevOps

Miro

Figma

Mixpanel

Amplitude

Tableau

Power BI

A/B testing tools

Job description

Key Responsibilities

• 1. Define and own the end-to-end product vision, strategy, and roadmap for AI-driven government services including conversational AI, document processing, voice-enabled systems, and citizen service automation aligned with Digital India goals and organizational objectives
• 2. Lead product discovery, market research, and user research to identify citizen pain points and opportunities for AI intervention across government departments.
• 3. Translate complex business requirements and government use cases into detailed product specifications, user stories, and acceptance criteria for technical teams
• 4. Oversee complete product lifecycle from ideation through launch, adoption, and continuousimprovement for 8-10 concurrent AI initiatives 5. Manage cross-functional collaboration between AI/ML engineers, data scientists, MLOps, UX designers, business analysts, and governmentstakeholders ensuring alignment and timely delivery
• 6. Define and track key product metrics (KPIs, OKRs) including user adoption, accuracy, response times, cost per transaction, and citizen satisfaction scores
• 7. Conduct competitive analysis of AI products in government sector, identify best practices, and drive product differentiation strategies
• 8. Lead go-to-market planning including rollout strategy, change management, training programs, and adoption campaigns for government employees and citizens
• 9. Manage product budgets, resource allocation, vendor evaluation, and contract negotiations forAI platform components
• 10. Knowledge of Responsible AI principles ensuring ethical AI deployment, bias mitigation, transparency, and compliance with MeitY guidelines and data protection regulations
• 11. Facilitate stakeholder reviews with government officials, executive leadership, and departmentheads presenting product progress, metrics, and strategic recommendations
• 12. Drive product innovation through experimentation with emerging AI technologies (LLMs, multimodal AI, agentic systems) evaluating feasibility for government applications.

Technical Competencies

• 1. 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 tradeoffs).
• 2. Product Management: Expert proficiency in Agile, Scrum, SAFe methodologies, productdiscovery frameworks (Design Thinking, Jobsto-be-Done), roadmap planning, backlogprioritization (MoSCoW, RICE, Kano model) 3. Tools & Platforms: Advanced proficiency in Jira, Confluence, Azure DevOps, Miro, Figma, product analytics platforms (Mixpanel, Amplitude), A/B testing tools
• 3. AI Product Lifecycle: Understanding of MLOps workflows, model deployment pipelines,monitoring for drift and performance, continuous learning systems, and production AI challenges
• 4. 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)
• 5. Technology Stack: Familiarity with cloud AI services (AWS Bedrock, Azure OpenAI, Google Vertex AI), conversational AI platforms, RAG architectures, voice processing systems, and APIbased integrations
• 6. Security & Compliance: Awareness of data classification, access control protocols, Personal Data Protection Act, and MeitY Responsible AI guidelines
• 7. Analytics & Metrics: Strong analytical skills using SQL, Excel, data visualization tools (Tableau, Power BI) for product analytics, cohort analysis, funnel optimization, and business impact measurement
• 8. Communication: Exceptional stakeholder management, executive presentation skills, ability to translate technical AI concepts for nontechnical audiences, and facilitate alignment across diverse teams.