Engineering Manager – AI Engineering

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).