# Engineering Manager – AI Engineering at Meesho Engineering Manager – AI Engineering at Meesho in Bangalore, Karnataka. On-site, full-time. Posted July 31, 2026. - Company: Meesho - Location: Bangalore, Karnataka - Workplace: On-site - Job type: Full-time - Experience: 9+ years - Posted: 2026-07-31 - Apply: https://jobs.lever.co/meesho/a1cc7ad9-a233-4ba6-b53b-e56116f6ac27/apply - Source: https://jobmagic.ai/jobs/engineering-manager-ai-engineering-meesho-7jydbd ## Description ### **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. - Push the frontier across MLOps, LLMOps, compute efficiency, and distributed ML systems. - Partner with Product, Data Science, and Platform teams to turn AI capabilities into production impact for millions of users. - Own the team's operating rhythm: hiring, performance management, sprint planning, and OKRs. ### What You'll Need - Bachelor's or Master's in Computer Science or a related field. - 9+ years of software engineering experience, including 2+ years managing engineers. - Strong hands-on experience with the modern LLM inference stack — TensorRT-LLM, vLLM, SGLang — and with production, low-latency model serving at scale. - Depth in inference optimization: GPU kernel tuning, quantization, speculative decoding, KV-cache and memory/IO optimization. CUDA / GPU programming experience is a strong plus. - Experience with distributed training and the frameworks behind it — PyTorch FSDP, DeepSpeed, Megatron, or Ray. - Experience running GPU fleets in production — Kubernetes (ideally GKE), GPU scheduling and allocation, and multi-region/multi-cluster deployment. - Familiarity with building LLM-powered agents and agentic workflows, and a point of view on where autonomy can replace manual engineering toil. - Experience with big-data and streaming stacks — Spark, Flink, or similar. - Proficiency in Python; systems-level fluency (C++ / Go / Rust) for performance-critical paths. - Strong leadership, problem-solving, and stakeholder-management skills. ### **Preferred** - Open-source contributions to inference engines, training frameworks, or ML infra tooling. - Experience managing GPU cost/efficiency (FinOps) for a large fleet on Cloud and Neo-Clouds.  - Track record building platforms for high-scale consumer products (millions of users). - Familiarity with observability and reliability for ML systems (SLOs, autoscaling, incident response).