Lead MLOps Engineer

Cloudkeeper; Garhi; On-site; INR 3,800,000 - 6,400,000; Full time

Job summary

Cloudkeeper seeks a senior ML/AI infrastructure leader to drive R&D and engineering for GPU-driven optimization in its FinOps for AI platform. You will design engines for right-sizing, idle shutdown, and batching, and extend to LLM workloads with caching, routing, and dynamic batching.

You will partner with Lens AI to translate signals into customer-ready optimization recommendations, collaborate with product and customer success, set engineering standards, and mentor a growing ML/MLOps team.

Qualifications

• 7+ years hands-on engineering experience (B.E./B.Tech./M.Tech./MCA)
• Production GPU workloads experience with measurable optimization
• Strong Python, Linux and systems fundamentals
• ML model lifecycle: training, serving, inference
• MLOps fluency: deployment, monitoring, observability, GPU ops
• Hands-on with cloud GPU instances and Kubernetes GPU orchestration
• Familiarity with modern LLM inference frameworks
• Lead experience managing 3+ engineers

Responsibilities

• Drive R&D and engineering for AI infrastructure optimization on GPU/ML workloads
• Design optimization engines for GPU right-sizing, idle shutdown, batching
• Extend optimization stack to LLM workloads: caching, routing, dynamic batching
• Translate GPU/ML signals into actionable optimization recommendations
• Collaborate with product, platform and customer success to ship features
• Set engineering standards and mentor ML/MLOps engineers
• Hire and grow a team of ML infrastructure engineers

Skills

Python

Linux

ML lifecycle

MLOps

Kubernetes

Leadership

Communication

Education

B.E./B.Tech./M.Tech./MCA with 7+ years experience

Tools

Kubernetes-based GPU orchestration

NVIDIA GPU Operator

Run:ai

vLLM

Job description

Responsibilities:

• Drive R&D and engineering for AI Infrastructure optimization within CloudKeeper's FinOps for AI platform building the Tuner AI / Commit AI capability on GPU and ML workloads
• Design and build optimization engines for GPU right-sizing, idle shutdown, spot migration with checkpoint/resume automation, inference batching, quantization, and model placement
• Extend the optimization stack to LLM-era workloads — caching, model routing, dynamic batching, prompt optimization, RAG-aware architectures
• Partner with the Lens AI team to translate GPU and ML workload signals into actionable, dollar-quantified optimization recommendations for customers
• Work cross-functionally with product, platform, and customer success teams to ship optimization features end-to-end (data ingestion optimization engine customer-facing recommendation)
• Lead technical direction for AI workload optimization, set engineering standards, and mentor the ML / MLOps engineering bench as the AI Infrastructure pillar scales
• (Lead level) Hire, ramp, and grow a team of ML infrastructure engineers as headcount expands

Must Have:

• B.E / B.Tech / M.Tech / MCA with 7+ years of hands‑on engineering experience
• Production experience with GPU workloads — has measurably optimized GPU utilization, throughput, or cost in a real production environment, not just academic / lab work
• Strong performance engineering background — must come ready with a concrete optimization story including before/after metrics (latency, throughput, or cost reduction)
• Strong Python + Linux + systems fundamentals
• Solid understanding of the ML model lifecycle — training, serving, inference — able to reason about what is running on the GPU and why
• MLOps fluency — model deployment, monitoring, observability, GPU cluster operations
• Hands‑on with cloud GPU instances (AWS P5 / G6, Azure ND series, GCP A3, or equivalent) and Kubernetes-based GPU orchestration (EKS / AKS / GKE GPU node pools, Karpenter, Run:ai, NVIDIA GPU Operator, or similar)
• Familiarity with at least one modern LLM inference framework — vLLM, TGI, Triton, SGLang, Ray Serve, or BentoML
• Strong communication skills — able to translate deep technical optimization into customer / business outcomes
• (Lead level) Experience managing or technically leading a team of 3+ engineers

Good to Have:

• Deep LLM-era optimization expertise — KV caching, semantic caching, model routing, dynamic batching, quantization (FP16 INT8 INT4), model distillation, structured outputs
• Familiarity with LLM workload patterns — RAG, agents, embeddings, vector databases (Pinecone, Weaviate, Qdrant)
• CUDA, NCCL, mixed‑precision training and inference
• Experience with managed ML training platforms — SageMaker, Azure ML, Vertex AI, Databricks Mosaic
• Exposure to GPU‑native clouds — CoreWeave, Lambda Labs, RunPod, Crusoe
• Open source contributions to ML infrastructure projects — vLLM, llama.cpp, TGI, Ray, Triton, KubeRay
• Adjacent experience in cloud cost optimization / FinOps — Spot.io, ScaleOps, Granulate, CAST AI
• Comfort with Agile methodology and modern engineering practices (CI/CD, code review, observability)