On-prem Platform Engineer

On-prem Platform Engineer

Location: Charlotte, NC

Key Skills:

Must-Have Skills (Mandatory Keywords)

LLM Inference & Optimization

• vLLM, TensorRT-LLM, Triton Inference Server, SGLang
• Inference optimization techniques:
  • Continuous batching
  • Speculative decoding
  • KV cache / Prefix caching
• Model optimization:
  • FP8, AWQ, GPTQ

Distributed & GPU Systems

• Tensor parallelism and large model scaling
• CUDA, NCCL, GPU architecture
• GPU partitioning & optimization (MIG)

Kubernetes & ML Serving

• Kubernetes-based ML serving platforms
• KServe, OpenShift AI
• Helm charts, Operators, platform automation

GPU Orchestration

• Run:AI or similar GPU scheduling/orchestration platforms
• Multi-tenant GPU workload management

Platform Engineering

• Experience building internal AI/ML platforms (on-prem or hybrid)
• Strong automation and system design mindset

Observability & Performance

• Prometheus, Grafana
• ML observability (model latency, throughput, drift, resource utilization)
• Performance benchmarking and tuning

Good to Have / Preferred Skills

• Experience with LLMOps / GenAI pipelines
• Exposure to hybrid cloud (on-prem + GCP/Azure integration)
• Familiarity with Inferentia / alternative accelerators
• Knowledge of service mesh / networking in GPU clusters

Build, configure, and operate on prem Kubernetes/OpenShift AI platforms for deploying and serving GenAI models and LLM inference workloads.

Design and optimize high performance inference stacks using vLLM, TensorRT LLM, Triton Inference Server, SGLang, and advanced techniques (continuous batching, speculative decoding, KV caching).

Manage GPU orchestration and capacity using Run:AI, MIG, CUDA/NCCL, and tensor parallelism to maximize utilization and throughput.

Deploy and operate Kubernetes ML serving frameworks (KServe, Helm, Operators) for scalable, reliable model serving.

Drive inference optimization and benchmarking, leveraging FP8, AWQ, GPTQ, and performance tools such as GuideLLM and Locust.

Implement observability and ML monitoring using Prometheus, Grafana, Arize AI, ensuring SLA/SLO compliance for GenAI services.

Collaborate with ML and research teams to onboard new models, tune inference performance, and productionize GenAI use cases.