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Engineering Manager – AI Engineering

Meesho· Tech – Demand
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Bangalore, KarnatakaFull Time EmployeePosted 2mo ago

About the role

Engineering Manager role to lead scalable AI platforms and infrastructure, managing high-performing AI engineering teams to design, deliver, and optimize production-grade AI systems powering Meesho across use cases.

Responsibilities

  • 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.

Qualifications

  • 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.

Skills

Required:AI platform architecture and scalabilityInference optimizationOpen-weight model optimizationMLOps / LLMOpsLeadership & people management

Benefits

Last updated Oct 21, 2026. Interested? Apply on the company site.
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