Senior ML Infrastructure Engineer

Orion Innovation • Edison, NJ

1d

About The Position

We're building a large-scale document intelligence platform that processes text files up to 5 TB in size, extracts insights using BERT-class NLP models, and surfaces answers to analysts via a low-latency query interface. The platform runs on Azure Kubernetes Service (AKS) with dedicated GPU node pools, uses KEDA for event-driven autoscaling, and integrates with Azure Data Lake Storage Gen2 and Azure OpenAI. This is a hands-on role that sits at the intersection of platform engineering and applied ML, and requires someone who is equally comfortable debugging a CUDA out-of-memory error and designing a Kubernetes autoscaling policy. As the Senior ML Infrastructure Engineer the resource will own the end-to-end infrastructure layer — from GPU cluster configuration and CUDA runtime management to Kubernetes job orchestration and model serving.

Requirements

• Kubernetes / AKS Expert: Multi-node-pool design, taint/toleration, autoscaler, GPU node pools (NC/ND series)
• Device plugin, driver compat, resource limits, KEDA
• Scaled Job, queue triggers, cooldown tuning
• CUDA / cuDNN
• Runtime config via PyTorch; raw kernel dev not required
• PyTorch (GPU inference)
• Batching, FP16, memory management, profiling
• Hugging Face Transformers
• BERT/DistilBERT/BGE loading, pipeline API, tokenization
• Python (production)
• Async workers, Azure SDK, queue consumers
• Azure infrastructure
• VNet, private endpoints, Key Vault, ADLS, AD
• Docker / Helm
• Multi-stage builds, Helm chart authoring
• IaC (Terraform / Bicep)

Nice To Haves

• Willingness to learn is acceptable

Responsibilities

• Own the end-to-end infrastructure layer — from GPU cluster configuration and CUDA runtime management to Kubernetes job orchestration and model serving.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed