WebinarFrom Commitments to Outcomes: Build a Smarter Cloud Commitment Strategy
Oct 14th, 12:00 PM EST 5:00 PM BST
7-12 Years NoidaResponsibilities:
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
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 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
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