AI/ML Platform Engineer Containers & GPU Infrastructure - NJ

Hybrid: 4 days onsite, 1 remote.
Type: Contract-to-Hire

About the Role

We're hiring a hands-on AI/ML Platform Engineer to build and run the infrastructure behind enterprise AI/ML services at a large financial services organization. The core of the job is building containers: writing, hardening and shipping container images and GPU-enabled workloads, then running them on Kubernetes, NVIDIA GPU infrastructure and Microsoft Azure.

This is not a DevOps-only role, and it is not model development. You'll build, operate, troubleshoot and improve the platform that data science and application teams depend on, inside a secure, regulated environment.

What You'll Do

Container Engineering & Kubernetes

• Build, harden and maintain container images for AI/ML workloads, including GPU-enabled images with the correct NVIDIA driver and CUDA dependencies.
• Manage images in registries with versioning, vulnerability scanning and lifecycle controls.
• Deploy, scale and support containerized AI/ML services on enterprise Kubernetes clusters.
• Allocate GPU resources across containers and clusters and keep them well used.
• Troubleshoot container runtime, networking, storage, performance and orchestration issues.
• Automate container provisioning, configuration and lifecycle management.
• Partner with application and platform teams to deploy containerized AI/ML services.

GPU Infrastructure

• Support bare-metal GPU servers used for training, inference and production workloads.
• Install, configure and troubleshoot NVIDIA GPU drivers, CUDA and NVIDIA AI Enterprise software.
• Monitor GPU utilization, availability, performance and allocation.
• Resolve driver, container-compatibility and workload-performance issues.
• Integrate GPU infrastructure with enterprise AI/ML platforms.

Azure AI/ML Platform

• Design, build and maintain AI/ML platform services on Azure, including Azure AI services.
• Build MLOps/AIOps pipelines for training and production models.
• Support provisioning, administration, interoperability, disaster recovery and business continuity.
• Troubleshoot Azure infrastructure, DNS, connectivity, NSGs and routing.
• Help application teams use AI services to meet business goals.
• Keep platform services within enterprise availability and performance targets.

Automation, IaC & CI/CD

• Build platform automation with Python, Terraform, GitHub Actions, Azure CLI and the Azure Python SDK.
• Maintain Infrastructure as Code using Terraform and ARM templates.
• Build and maintain CI/CD pipelines for infrastructure, container images and AI/ML services.
• Develop and consume APIs, SDKs and webhooks to connect orchestration platforms with enterprise systems.
• Keep improving platform tooling and engineering practices.

Security & Compliance

• Work with cloud, network, cybersecurity and risk teams to deliver secure AI/ML solutions.
• Keep cloud and container infrastructure compliant with internal policy and regulatory requirements.
• Apply Azure security controls: authentication, RBAC and data protection.
• Support PKI and certificate management for cloud services and applications.
• Identify and reduce risks across cloud infrastructure, containers and AI/ML workloads, including the secure deployment of models and data.
• Take part in monitoring, incident resolution and operational support.

Required

• Hands-on experience in cloud platform, infrastructure or AI/ML platform engineering.
• Proven experience building container images (Docker or equivalent) and deploying containerized workloads.
• Strong Kubernetes and container orchestration experience.
• Experience supporting GPU infrastructure and GPU-enabled containers, including bare-metal servers, NVIDIA drivers and CUDA.
• Familiarity with NVIDIA AI Enterprise.
• Strong Microsoft Azure experience.
• Proficiency in Python for scripting and automation.
• Hands-on Terraform, IaC and CI/CD experience, including GitHub Actions and GitHub Runners.
• Solid understanding of networking, DNS, routing, cloud connectivity and infrastructure security.
• Working knowledge of model deployment and MLOps concepts.
• Strong troubleshooting and problem-solving skills.
• Able to work independently and with cross-functional teams.

Nice to Have

• AI/ML infrastructure experience in a large enterprise or financial services organization.
• Production experience running NVIDIA GPU infrastructure and GPU-enabled Kubernetes clusters.
• Experience with GPU scheduling, utilization monitoring and performance tuning.
• Hands-on NVIDIA AI Enterprise experience.
• Experience with Azure AI services and enterprise AI/ML platforms.
• Background in enterprise security, governance and regulatory compliance.
• Experience in Agile/Scrum and DevOps environments.