Senior Product Manager – AI & ML

Role: Product Manager Location: Gurugram (Sector 32) About Group Bayport Group Bayport, headquartered in Atlanta (USA), is a rapidly growing global e-Commerce B2B2C, B2B and B2C organization that has re-defined the business of delivering high-quality custom products through a unique blend of cutting-edge digital technologies, robust manufacturing capabilities and global supply chain. An industry leader in print technology, Group Bayport operates in the United States, Canada, Australia, New Zealand, UK, and India through its family of brands – BannerBuzz, Covers & All, Vivyx Printing, Circle One, Giant Media, Neon Earth, Optamark and Northcape. While our advanced technology and customer focus are certainly our salient attributes, our true strength comes from our team of 2000+ people. Having scaled to USD 150mn in 14 years, we believe we are at the cusp of exponential and disruptive growth ahead. For more about Group Bayport, please visit Website: https://groupbayport.com/about-us/ LinkedIn: https://www.linkedin.com/company/groupbayport/mycompany/ Job Overview We are looking for a Senior Product Manager to define, build, and scale AI- and ML-powered products and capabilities across Group Bayport. The role will own the product lifecycle from opportunity discovery and problem definition through solution design, experimentation, launch, adoption, and continuous improvement. The successful candidate will work closely with engineering, data science / ML engineering, UI/UX, analytics, business, marketing, and operations teams to turn AI capabilities into secure, reliable, measurable business outcomes. Focus areas may include Generative AI, agentic workflows, AI-assisted design and personalization, intelligent automation, customer-facing AI assistants, predictive models, and AI-enabled quality and productivity solutions. Key Responsibilities • AI Product Strategy & Opportunity Discovery • Define product vision, strategy, roadmap, and success metrics for AI/ML products aligned with business priorities and customer needs. • Identify and prioritize high-value AI opportunities across e-commerce journeys, product customization, design, customer support, marketing, operations, and product engineering. • Translate business problems into clearly scoped product opportunities; assess customer value, feasibility, expected impact, cost, risk, and time to value. • Conduct user research, workflow discovery, competitive analysis, and build-versus-buy assessments for AI capabilities and vendors. • Product Definition & End-to-End Delivery • Own the product lifecycle from discovery and MVP definition through PRDs, user stories, acceptance criteria, release planning, launch, adoption, and iteration. • Partner with engineering and data teams to define solution approaches, integrations, data requirements, model/service dependencies, and non-functional requirements. • Create clear human-in-the-loop workflows, fallback paths, exception handling, and user experiences for AI-assisted and agentic products. • Manage the backlog, dependencies, prioritization, stakeholder alignment, and delivery through agile product development practices. • Generative AI, Agentic AI & ML Applications • Drive products using appropriate AI approaches, such as LLMs, retrieval-augmented generation (RAG), AI agents, text/image generation, recommendations, classification, forecasting, or predictive models, based on the problem to be solved. • Explore and productize use cases such as AI-generated designs and templates, pre-sales/post-sales assistants, intelligent search and recommendations, automated content workflows, artwork readiness checks, and AI-assisted QA or engineering productivity. • Work with technical teams to define evaluation methods for output quality, relevance, accuracy, latency, reliability, and cost; compare model/provider options where appropriate. • Ensure AI experiences are understandable and usable, with suitable confidence cues, user controls, feedback capture, and escalation to human support when needed. • Data, Measurement & Business Outcomes • Define measurable KPIs and baselines, such as conversion and engagement, task completion, customer satisfaction, adoption, resolution rate, productivity, quality, cycle time, and cost per task. • Use analytics, experiments, A/B tests, and qualitative feedback to validate hypotheses and assess incremental impact. • Establish monitoring for AI product quality and operational performance, including failure rates, latency, usage, cost, drift where applicable, and user feedback. • Build business cases and communicate expected benefits, assumptions, investment needs, realized outcomes, and scale-up recommendations. • Responsible AI, Governance & Operational Readiness • Partner with security, legal, data, and technology stakeholders to ensure appropriate privacy, data protection, access controls, security, and regulatory compliance. • Contribute to common AI product standards for model/vendor selection, data handling, evaluation, prompt and knowledge management, monitoring, and release readiness. • Address risks such as inaccurate or fabricated outputs, bias, prompt injection, data leakage, unsafe actions, and inappropriate automation through suitable safeguards and testing. • Define operational ownership, support processes, incident escalation, auditability, documentation, and ongoing review for production AI products. • Cross-Functional Collaboration & Communication • Align business stakeholders, engineering, data science / ML engineering, UX/UI, analytics, and operations around a shared product outcome. • Explain AI capabilities, limitations, trade-offs, and risks clearly to technical and non-technical audiences. • Maintain product documentation, roadmaps, decision logs, release notes, and knowledge repositories. • Drive adoption and change management through stakeholder enablement, user feedback loops, and clear communication. Qualifications & Experience • Bachelor’s degree in Computer Science, Engineering, Information Technology, or a related field; MBA or relevant product-management qualification is an advantage. • Typically 5+ years of product management / product ownership experience, including meaningful experience delivering data, AI, ML, or automation-enabled products. Senior-level candidates should demonstrate ownership of complex products and cross-functional delivery. • Demonstrated ability to take products from discovery and MVP through launch, adoption, measurement, and iteration. • Experience translating ambiguous business problems into product requirements, prioritizing roadmaps, and aligning multiple stakeholders. • Working understanding of AI/ML concepts and product implications, including LLMs, RAG, embeddings/vector search, AI agents, model evaluation, and predictive ML. • Experience defining product metrics, interpreting analytics, and using experimentation or structured evaluation to guide decisions. • Strong written and verbal communication skills, with the ability to communicate technical concepts, assumptions, limitations, and trade-offs. Technical & Product Skills • Product tools: Jira or similar backlog-management tools; Figma or equivalent collaboration/design tools; product documentation and roadmap tools. • AI/ML literacy: Familiarity with LLM APIs, prompt design, RAG, vector databases, agentic workflows, model evaluation, and AI product observability. • Data literacy: Ability to define KPIs, work with dashboards and analytics, understand data quality and experimentation, and partner effectively with data teams. • Technical fluency: APIs, integrations, cloud-based products, data flows, authentication/access controls, and key non-functional requirements. • Preferred: Experience in e-commerce, personalization, digital customer journeys, design/content automation, customer support automation, or enterprise AI platforms. • Preferred: Exposure to AI governance, privacy/security reviews, model/vendor evaluation, production monitoring, or human-in-the-loop design. Behavioural Competencies • Strategic thinking and customer-centric problem solving. • Structured execution, prioritization, and ownership. • Comfort with ambiguity, experimentation, and iterative learning. • Stakeholder management and cross-functional collaboration. • Analytical thinking and outcome orientation. • Innovation, curiosity, and continuous learning. • Attention to detail and responsible decision-making.