Back to SearchProduct Manager - AI
Office in India: Gurugram, & 5 others
Product Management& 4 others
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We are looking for an experienced Product Manager - AI to lead the vision, development, and delivery of AI-driven products, bridging the gap between market needs, business objectives, and technical execution. The ideal candidate will bring strong product leadership skills combined with deep technical acumen in AI and machine learning to drive innovative solutions from concept to production.
Responsibilities
- Define and communicate product vision and mission to align stakeholders around a shared goal
- Lead product development initiatives with an entrepreneurial mindset to identify new opportunities
- Serve as the bridge between market demands and business strategy to ensure product-market fit
- Own product and service design processes to deliver user-centric solutions
- Develop and execute product and go-to-market strategies for successful product launches
- Build business models and financial projections to support product decisions
- Manage the product roadmap to ensure alignment with business priorities and timelines
- Conduct market and competitor analysis to inform strategic product decisions
- Oversee backlog and change management processes to maintain delivery efficiency
- Track and report on product success metrics to measure impact and inform iteration
- Apply agile delivery methodologies to manage product development cycles
- Own AI solutions from concept to production, ensuring responsible and ethical AI practices throughout the lifecycle
Requirements
- 10-15 years of overall experience in IT, with 10+ years specifically in Product Owner and Product Management roles
- Expertise in BFSI, Healthcare, Insurance, and Life Sciences domains
- Expertise in BPM, wireframing, mockup design, and demo creation
- Expertise in product roadmap management, risk management, and mitigation strategies
- Knowledge of Machine Learning, Deep Learning, NLP, Computer Vision, and Generative AI / LLMs
- Understanding of model training versus model usage trade-offs, along with MLOps / LLMOps lifecycle management
- Understanding of dataset creation, labeling, quality considerations, AI evaluation metrics, and monitoring strategies
- Familiarity with advanced requirements engineering for AI systems and agile product ownership for AI and data teams
- Knowledge of responsible AI principles and ethics
- Familiarity with AI models used for productivity enhancement in product management activities