Product Manager AI/ML

The Product Manager AI/ML owns the continuous lifecycle, deployment, and strategic direction of company’s high-growth generative enterprise platforms . This individual acts as the central link between international corporate goals, complex technical execution, and client-facing product delivery. The role ensures that advanced artificial intelligence capabilities translate seamlessly into scalable, production-ready features ,

Detailed Duties & Responsibilities

• Roadmap and Vision Strategy: Architect and present long-term product vision for advanced Agentic AI, Generative AI, and unified communication channels. Define clear development timelines and translate market demands into specific machine learning goals
• Full Lifecycle PLM Governance: Oversee end-to-end Product Lifecycle Management (PLM) using data-focused frameworks Manage products from early R&D ideation, through training/validation phases, to full Business-As-Usual (BAU) transition .
• Cross-Border Technical Orchestration: Coordinate day-to-end technical sprint cycles with the engineering teams based at the Indian subsidiary (p. 6). Ensure local deployments match broader global infrastructure and strict data policies
• Commercialization & Budget Management: Design extensive commercial business cases for product variations Budget accurately for API compute consumption, cloud architecture costs, and specialized engineering needs
• KPI Modelling & Optimisation: Formulate performance indicators evaluating AI agent accuracy, model training drift, response times, and automated user resolution rates
• Cross-Functional Communication: Present complex, technical AI roadmaps into clear strategic summaries for board directors and external corporate stakeholders

Required Qualifications & Education

• Minimum Education: Bachelor’s Degree or Higher Degree in Computer Science, Data Science, Business, or a closely related technical discipline Mandatory Experience & Skills Level
• Experience Required: Minimum of 5 years of verifiable experience in data management, machine learning, or artificial intelligence product management
• Infrastructure Knowledge: Deep conceptual familiarity with data engineering infrastructure, analytics pipelines, and conversational AI mechanics
• Execution Capabilities: Proven history of launching, scaling, and iterating enterprise-grade, cloud-based data products
• Methodologies & Leadership: Advanced expertise in Agile/Scrum delivery styles, stakeholder engagement matrix systems, and mentoring complex cross-functional teams