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Build AI-powered enterprise products. Turn complex technology challenges into scalable platforms.
We’re looking for an experienced AI and Data Product Leader to own the strategy, productization, scaling, and commercial success of enterprise-grade AI, data modernization, and intelligent engineering platforms. You’ll take complex enterprise problems from 0→1 product incubation through product-market fit, enterprise adoption, and global scale.
What you’ll lead
Define product vision, market positioning, investment priorities, and multi-year roadmaps for AI, data, and modernization platforms.
Take products from concept and MVP through production deployment, enterprise adoption, and scale—building reusable capabilities, not one-off solutions.
Shape Generative AI, LLM, and multi-agent product capabilities, including human-in-the-loop controls, AI evaluation, observability, and responsible AI governance.
Drive enterprise data and legacy modernization through reusable frameworks for assessment, conversion, testing, validation, and migration.
Partner with CIOs, CTOs, Chief Data Officers, and enterprise architecture leaders to discover customer needs and validate product direction.
Work across engineering, AI/ML, data, cloud, sales, delivery, alliances, and executive teams to drive adoption and commercial outcomes.
Manage product priorities and portfolio performance, connecting adoption, customer value, pipeline, revenue influence, and ROI.
What we’re looking for
12–18+ years of technology/product experience, including significant enterprise product management leadership.
A track record taking products from 0→1 and scaling platforms for large enterprise customers.
Strong understanding of Generative AI/LLMs, multi-agent systems, AI evaluation, data engineering, data modernization, cloud migration, and enterprise architecture.
Ability to translate ambiguous customer problems into differentiated, reusable products with clear business value.
Strong customer discovery, roadmap prioritization, commercial orientation, and cross-functional leadership.
Experience in BFSI—banking, insurance, or financial services—is highly desirable.
You don’t need to be a hands-on AI engineer—but you should be technically fluent enough to challenge architecture decisions, evaluate AI approaches, and translate technology into enterprise-ready products.
Interested in building the next generation of AI-native enterprise platforms?
Work arrangement
No
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