Engineering Manager – AI Engineering

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.

What You'll Do:

-

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.