Cloud Infrastructure Engineer

Job Title: Cloud Infrastructure Engineer

Location: Charlotte, NC (5 Days onsite)

Duration: 12+ months

Primary Skills

• vLLM
• TensorRT-LLM
• Triton Inference Server
• SGLang
• Kubernetes ML Serving
• KServe
• OpenShift AI
• GPU Orchestration
• Google Cloud Platform
• Terraform
• Nvidia

Key Responsibilities

• Design and manage scalable AI/ML infrastructure for GenAI and LLM workloads.
• Deploy and optimize LLM inference pipelines using vLLM, TensorRT-LLM, Triton Inference Server, and SGLang.
• Implement inference optimization techniques including:

1. Continuous Batching
2. Speculative Decoding
3. KV Cache / Prefix Caching
4. FP8 / AWQ / GPTQ quantization
5. Tensor Parallelism

• Build and maintain Kubernetes-based ML serving platforms using KServe and OpenShift AI.
• Manage GPU orchestration and scheduling using technologies such as Run:AI, CUDA, NCCL, and MIG.
• Develop Helm charts, Kubernetes Operators, and platform automation for AI workloads.
• Conduct performance benchmarking and optimization for GPU-based inference systems.
• Implement monitoring and observability using Prometheus and Grafana.
• Collaborate with data science and ML engineering teams to productionize LLM models.
• Automate infrastructure provisioning and deployment using Terraform.

Required Qualifications

• 6+ years of experience in cloud engineering or platform engineering.
• Experience with LLMOps/MLOps platforms.
• Strong hands-on experience with Kubernetes and containerized AI/ML workloads.
• Experience with GPU infrastructure and distributed inference optimization.
• Proficiency in Google Cloud Platform cloud services and cloud-native architecture.
• Strong scripting/programming skills in Python.
• Experience with ML observability and production monitoring tools.
• Familiarity with OpenShift AI and enterprise Kubernetes ecosystems.

Preferred Qualifications

• Knowledge of GenAI frameworks and RAG architectures.
• Exposure to enterprise AI governance and security practices.