Machine Learning Infrastructure Engineer

Role Focus : Production ML Systems | GPU Orchestration | Inference at Scale

What You'll Actually Do (Not Buzzwords)

Infrastructure That Doesn't Break

• Design and maintain the backbone for training, fine-tuning, and deploying ML models that actually work in production
• Orchestrate GPU workloads on Kubernetes (EKS) with node autoscaling, intelligent bin-packing, and cost-aware scheduling (spot instances, preemptibles—you know the drill)
• Build CI / CD pipelines that handle ML code, data versioning, and model artifacts like a well-oiled machine (GitHub Actions, Argo Workflows, Terraform)

Production ML, Not Science Projects

Ship Fast, Sleep Well

What We're Really Looking For

Must-Haves (No Negotiation)

The "We're Very Interested" Signals

Nice to Have (But Seriously Nice)

What Makes This Role Different

Ownership. You're not a ticket-taker or a consultant passing through. You'll own infrastructure that powers real AI products, make architectural decisions that matter, and have the autonomy to build things the right way.

Impact. Your work directly affects model training speed, inference latency, GPU costs, and system reliability. You'll see the results of your optimizations in dollars saved and milliseconds gained.

Quality over speed. We value security, operational excellence, and sustainable systems. No "move fast and break things" chaos here—we move deliberately and build things that last.

The Reality Check

This role is not for you if :

This role is for you if :

Write to mlops@careerxperts.com to get connected!