AI Product Manager

SMC Group; Delhi; Presencial; INR 240.000 - 360.000; Jornada completa

Descripción de la vacante

SMC Group seeks a Product Manager 2 – Platform, Data Engineering to lead internal AI solutions and data platform initiatives. You will own the roadmap for platform-level AI capabilities and work with engineers to deliver usable products built on AI frameworks and data infrastructure.

You will collaborate with data engineering to define success metrics, adoption, and business impact, while balancing speed, cost, and reliability for AI-driven features.

Formación

• 3–5 years of experience as a Product Manager with exposure to AI/ML, data, or platform products.
• Fintech background is required.
• B.Tech, BCA, or MCA required; MBA is a plus.
• Track record of shipping end-to-end products from discovery to launch and iteration.

Responsabilidades

• Identify business problems across teams that can be solved with AI or data platform capabilities.
• Own the roadmap for platform-level AI and data products, from framing to shipped solutions.
• Evaluate and select approaches for AI frameworks and LLM providers with engineering.
• Collaborate with data engineering on data pipelines, quality, and infrastructure.

Conocimientos

Product management

AI/ML exposure

Data platform

SQL skills

Analytics

Stakeholder management

Fintech

Educación

B.Tech/BCA/MCA

MBA (plus)

Herramientas

RAG pipelines

LLM providers

Descripción del empleo

Product Manager 2 – P latform , Data Engineering

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.
• 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