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).