Product Manager - AI Solutioning

We are seeking an experienced Product Manager – AI Solutioning to drive product vision, strategy and development of innovative AI-powered solutions. In this role, you will act as the bridge between market needs and business goals, leading product development from concept to production while leveraging advanced AI and technical expertise across domains such as BFSI, Healthcare, Insurance and Life Sciences.

Responsibilities

• Define and communicate product vision, mission and roadmap to align teams and stakeholders
• Lead product development with an entrepreneurial mindset and end-to-end ownership
• Act as the bridge between market and business to inform product and service design
• Own product and go-to-market strategy across the full product life cycle
• Drive business modeling and financials to support product decisions
• Conduct market and competitor analysis to identify opportunities
• Manage product backlog and change management processes
• Track product success metrics to measure impact and outcomes
• Oversee risk management and mitigation throughout product delivery
• Own AI solutions from concept to production within Agile delivery frameworks
• Apply AI models to improve productivity in product management activities
• Ensure adherence to Responsible AI and ethics standards

Requirements

• 10-15 years of experience in IT with 10+ years in Product Owner and Product Management roles
• Expertise in BFSI, Healthcare, Insurance or Life Sciences domains
• Proficiency in BPM, Wireframing and Mockup design and demo
• Competency in Product Roadmap Management, Risk Management and Mitigation
• Background in Market and Competitor Analysis, Backlog and Change Management and Product Success Metrics
• Skills in Agile Delivery Methodologies and Agile Product Ownership for AI and data teams
• Knowledge of Machine Learning, Deep Learning and NLP concepts
• Expertise in Computer Vision, Generative AI / LLMs and model training vs model usage trade-offs
• Understanding of MLOps / LLMOps lifecycle, dataset creation, labeling and quality considerations
• Familiarity with AI evaluation metrics and monitoring strategies
• Capability to perform advanced requirements engineering for AI systems