Product Manager 2 – P latform , Data Engineering

- Rol e Overview

Platform 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.
- Solid 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

📌 AI Product Manager (Delhi)
🏢 SMC Group
📍 Delhi