| Location | Mount Laurel, NJ |
Industry: Banking & Financial Services / Technology
Hybrid: 4 days onsite, 1 remote.
Type: Contract-to-Hire
Our client, a leading financial services organization, is seeking an experienced Senior Kubernetes & AI Platform Engineer to design, implement, and manage secure, scalable GPU-enabled Kubernetes platforms across Azure Kubernetes Service (AKS), Google Kubernetes Engine (GKE), and Google Distributed Cloud (GDC).
The ideal candidate will have deep Kubernetes expertise, hands-on NVIDIA GPU experience, and a strong background in cloud automation, AI infrastructure, security, and production operations.
Design, deploy, and maintain production-ready GPU-enabled Kubernetes environments across AKS, GKE, and GDC.
Manage NVIDIA GPU Operator, drivers, device plugins, GPU scheduling, and lifecycle management.
Implement GPU monitoring, telemetry, and alerting using NVIDIA DCGM Exporter and enterprise observability tools.
Configure GPU sharing and resource allocation using MIG, time-slicing, and Dynamic Resource Allocation (DRA).
Automate infrastructure provisioning, deployments, and scaling using Terraform, Helm, GitHub Actions, and scripting.
Optimize GPU utilization, cluster autoscaling, workload performance, and infrastructure costs.
Implement Kubernetes security controls, RBAC, policy-as-code, and compliance standards, including CIS, STIG, and Wiz.
Troubleshoot Kubernetes, GPU, networking, storage, and container-related issues.
Support AI/ML workloads, including model training, inference, and distributed computing.
Develop technical documentation, operational runbooks, and disaster recovery procedures.
Collaborate with cloud, security, infrastructure, and AI/ML engineering teams.
7+ years of experience in Cloud Engineering, Platform Engineering, DevOps, SRE, or Infrastructure Engineering.
Strong hands-on experience designing and managing production Kubernetes environments using AKS, GKE, or GDC.
Practical experience with NVIDIA GPUs in Kubernetes, including GPU Operator, drivers, device plugins, DCGM, MIG, and GPU scheduling.
Strong knowledge of Kubernetes architecture, networking, storage, security, observability, and autoscaling.
Experience with Terraform, Helm, GitHub Actions, and Python, Go, or Bash scripting.
Experience implementing cloud security, Kubernetes RBAC, admission policies, and compliance controls.
Familiarity with Prometheus, centralized logging, monitoring, and incident response.
Strong Linux, containerization, networking, and infrastructure troubleshooting skills.
Excellent communication and cross-functional collaboration skills.
Experience supporting AI/ML infrastructure, MLOps, model training, and inference workloads.
Familiarity with NVIDIA H100/H200 GPUs, GPU performance optimization, and distributed AI computing.
Experience with KServe, NVIDIA NIM, Triton Inference Server, Ray, Kubeflow, or MLflow.
Knowledge of Azure Machine Learning and Google Vertex AI.
Experience with Wiz, cloud security, and regulated financial services environments.
Certifications such as CKA, CKS, CKAD, Azure, Google Cloud, or NVIDIA.
A hands-on Kubernetes and AI infrastructure engineer with proven experience deploying and managing GPU-enabled cloud platforms. The successful candidate will combine strong technical architecture skills with automation, security, performance optimization, and production support expertise.