AI Product Manager

Product Manager 2 – P latform , Data Engineering

• Rol e OverviewPlatform Product Manager for the Platform, AI Solutions and Data Engineering team.

This role owns product decisions for internal AI and data platform capabilities —

identifying business problems across the company and turning them into practical,

usable products built on AI frameworks, multi-language-model tooling, and the

underlying data infrastructure.

• Key Responsibilities• Identify business problems across teams (product, operations, compliance, customer

support, etc.) that can be solved with AI or data platform capabilities.

• Own the roadmap for platform-level AI and data products, from problem framing through to

a shipped, adopted solution.

• Evaluate and select the right approach for a given problem — including which AI frameworks,

models, or LLM providers to use — in partnership with engineering.

• Work closely with the data engineering team on the underlying data pipelines, quality, and

infrastructure that platform products depend on.

• Define success metrics for platform products and track adoption, accuracy, and business

impact post-launch.

• Write clear product requirements and manage trade-offs between speed, cost, and reliability

for AI-driven features.

• Requ i r ed Qualifications & Experience• 3–5 years of experience as a Product Manager, with prior exposure to AI/ML, data, or

platform products preferred.

• Fintech background required.
• B.Tech, BCA, or MCA required; MBA is a plus.
• Track record of shipping products end-to-end — from problem discovery through launch and

iteration.

• A I & Data Technical Skill s• Good working understanding of AI frameworks and how they're applied to build products

(e.g. RAG pipelines, agents, fine-tuning vs. prompting trade-offs).

• Familiarity with data science fundamentals — how models are evaluated, common pitfalls

(bias, overfitting, data drift), and how to read model performance metrics.

• Experience working with multiple large language models (LLMs) across providers, and an

understanding of how to choose between them for a given use case (cost, latency, accuracy,

context window).

• Basic SQL skills — able to query and explore data independently rather than relying entirely

on engineering or analytics.

• Strong analytics skills, with the ability to explore data and build visualizations to

communicate insights clearly to both technical and business stakeholders.

• Business & Product Skills• Able to understand a company's business problems — across functions, not just technical

ones — and translate them into practical, well-scoped product opportunities.

• Comfortable prioritizing between multiple potential AI/data use cases based on business

impact, feasibility, and effort.

• Strong stakeholder management — able to work with both technical teams (engineering,

data science) and business teams (operations, compliance, other functions) to align on what

to build