Product Owner - GEN AI

STL Digital; Bengaluru; Hybrid; INR 2,500,000 - 4,000,000; Full time

Job summary

STL Digital in Bengaluru leads two enterprise GenAI products, defining vision, roadmaps, and KPIs to transform global life sciences workflows. The role emphasizes user-centric design, AI-powered requirements, and end-to-end lifecycle management within Agile environments.

The candidate will drive governance, compliance, and responsible AI practices while collaborating with engineers, business leaders, and compliance teams to ensure ROI and adoption metrics are achieved.

Qualifications

• Defined product vision and KPIs for enterprise GenAI products
• Led discovery with user-centric research
• Managed end-to-end AI product lifecycle with Agile/Scrum
• Embedded responsible AI practices and regulatory alignment
• Tracked adoption KPIs and ROI with cross-functional teams

Responsibilities

• Defined product vision, roadmap, and KPIs for two GenAI products.
• Led product discovery through user-centric research and AI-powered requirements.
• Owned end-to-end AI product lifecycle from ideation to deployment.
• Managed AI governance, compliance, and responsible AI practices.
• Drove backlog prioritization and improvements with cross-functional teams.

Skills

Product strategy

GenAI & AI product lifecycle

Agile/Scrum

Market research

Stakeholder management

Compliance & governance

Tools

LLM orchestration

RAG architectures

Job description

Role & responsibilities

• Defined product vision, roadmap, and KPIs for two enterprise GenAI products supporting global business workflows in a life sciences environment; conducted market research, and feasibility assessments to inform strategic roadmap decisions.
• – Led product discovery through user-centric design research, translating complex business challenges into AI-powered user stories, acceptance criteria, and product requirements aligned with enterprise digital transformation goals.
• – Owned end-to-end AI product lifecycle — from ideation and prototyping to deployment, change management, and continuous optimization using Agile/Scrum methodologies; drove experimentation and iterative model improvement through structured A/B evaluation cycles.
• – Managed risks, dependencies, and compliance requirements related to AI systems; partnered with compliance and governance teams to embed responsible AI practices — including fairness, transparency, and data governance — aligned with life sciences regulatory standards.
• – Drove backlog prioritization and iterative improvements in collaboration with cross-functional teams including, engineers, and business leadership; championed AI capabilities across the organization through structured change management programs. Monitored product performance using analytics and user feedback to track adoption KPIs, feature ROI, and inform roadmap decisions; maintained working knowledge of AI/ML trends, tools, and best practices including LLM orchestration and RAG architectures.