Responsibilities

- Drive R&D; and engineering for AI Infrastructure optimization within CloudKeepers 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

Requirements

- 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 workplace,



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

Disclaimer : This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

📌 Lead - ML Ops (Uttar Pradesh)
🏢 CloudKeeper
📍 Uttar Pradesh