Senior Engineering Manager AI

Role Overview

The Senior AI EM is a new leadership role within Flipkarts AI Solutions Org, established to bridge the gap between AI engineering execution and strategic business impact. You will own the people, process, and technical direction for a team of 8–15 AI Engineers, ML Engineers and Software Development Engineers who build production-grade AI applications at Flipkart scale.

This is not a purely people-management role. You are expected to be technically deep — capable of reviewing system designs, challenging architectural decisions, and guiding your team through the complexities of deploying LLMs, agentic workflows, and ML pipelines in a high-pace, large-scale production environment. At the same time, you will operate as a strategic partner to product, business, and data science leaders, translating ambiguous business problems into well-scoped AI solutions.

What Makes This Role Unique

• AI-Native DNA: You will champion and evolve AI-first development practices (Copilot, Cursor, LLM-assisted code review, automated testing) across your organisation.
• Full-Stack AI Ownership: Unlike traditional EM roles scoped to backend or frontend, you own the entire vertical — from database design and API development to RAG pipelines, agentic systems, model serving, and user-facing interfaces.
• Business × Technology Bridge: You will be the primary engineering partner for non-tech stakeholders (Finance, HR, Cataloguing, Supply Chain), owning the full SDLC for AI-powered automation across these functions.
• Research-to-Production Pipeline: Working closely with Flipkarts Data Science team, you will ensure that research breakthroughs translate into scalable, production-grade systems.

Core Responsibilities

• Technical Leadership & Architecture
• People Leadership & Team Building
• Business Impact & Stakeholder Partnership
• Operational Excellence

Qualifications Required

• B.Tech / M.Tech / M.S. / Ph.D. in Computer Science, Machine Learning, Artificial Intelligence, Statistics, or a closely related field.
• 14+ years of overall experience in software engineering, machine learning, or applied AI — with at least 3–4 years in a people management role leading teams of 6+ engineers or data scientists.
• Demonstrated experience shipping production ML/AI systems at scale — not just prototypes, but systems serving real users with measurable business impact.
• Strong technical depth across the modern AI stack: LLMs, transformer architectures, RAG, vector databases, model serving infrastructure, and ML pipelines.
• Proficiency in at least two of: Python, Java, Go — with hands-on experience in ML frameworks (PyTorch, TensorFlow) and cloud-native deployment (Docker, Kubernetes).
• Proven ability to operate at the intersection of technology and business — translating complex technical concepts for non-technical stakeholders and influencing product/business strategy.
• Experience with distributed systems, microservices architecture, and high availability production environments.