Meesho - Engineering Manager - Artificial Intelligence

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.

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.

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)

Note : For your candidature to be considered on this job, you need to apply necessarily on the redirected career page of the company as well.