Engineering Manager - AI Engineering

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 optimisation of production-grade AI systems powering AI use cases across Meesho.

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 optimisation across the full stack: GPU kernel tuning, quantisation (including outlier/tail-distribution handling), and memory/IO-bandwidth optimisation while building agents that codify and delegate known optimisation procedures.
• Optimise open-weight models at both the model and inference-engine level: distillation, quantisation, 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.

Requirements:

• 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 optimisation: GPU kernel tuning, quantisation, speculative decoding, KV-cache and memory/IO optimisation. 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 such as 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).