Engineering Manager – AI Engineering

Overview

About Meesho Meesho is India's fastest-growing internet commerce company, on a mission to democratize e-commerce for everyone. We serve millions of customers and over 1.75 million sellers through technology-driven innovation, building the scalable systems that power Meesho's most critical surfaces — Search, Recommendations, Personalized Ranking, Logistics, Fraud Detection, and Image Match. The AI

What you'll do

• About Meesho Meesho is India's fastest-growing internet commerce company, on a mission to democratize e-commerce for everyone.
• We serve millions of customers and over 1.75 million sellers through technology-driven innovation, building the scalable systems that power Meesho's most critical surfaces — Search, Recommendations, Personalized Ranking, Logistics, Fraud Detection, and Image Match.
• The AI Platform sits at the heart of this.
• It serves a peak of 1M+ real-time deep-learning model inferences per second on ordinary days, scaling 3x+ on sale days — with the reliability that scale demands.
• The team works at the frontier of applied AI and infrastructure — multi-region inference, novel embedding-search algorithms, and optimized open-weight LLM models — squeezing out every bit of computation and passing the cost savings straight back to customers.
• About the Role We are looking for an experienced Engineering Manager – AI Engineering to lead the development of scalable AI platforms and infrastructure while managing high-performing engineering teams.
• You will drive the design, delivery, and optimization of production-grade AI systems powering AI use cases across Meesho..

Requirements

• Lead, mentor, and grow a team of AI engineers — setting technical direction, raising the engineering bar, and owning execution and delivery end to end
• Architect and scale Meesho's AI platform: cross-region model inference, multi-GPU fleet allocation and management, distributed training, and feature-engineering infrastructure
• Drive inference optimization across the full stack — GPU kernel tuning, quantization (including outlier/tail-distribution handling), and memory/IO-bandwidth optimization — while building agents that codify and delegate known optimization procedures
• Optimize open-weight models at both the model and inference-engine level — distillation, quantization, speculative decoding, KV-cache and serving-engine tuning
• Scale data-science productivity through autonomous, agent-driven workflows spanning feature engineering, model training, and rollout
• Push the frontier across MLOps, LLMOps, compute efficiency, and distributed ML systems

Skills