Sr Kubernetes & AI Platform Engineer - NJ

Industry: Banking & Financial Services / Technology
Hybrid: 4 days onsite, 1 remote.
Type: Contract-to-Hire

Position Overview

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.

Key Responsibilities

• 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.

Required Qualifications

• 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.

Preferred Qualifications

• 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.

Ideal Candidate

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.