MLOps Engineer- Billion Dollar US Enterprise Software - Hiring in India!

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

Partner with Data Scientists and ML Engineers to turn Jupyter notebooks into production-grade systems

Deploy and scale inference backends (vLLM, Hugging Face, NVIDIA Triton) that serve real traffic

Optimize GPU utilization because every idle A100 hour is money burning

Build observability that actually tells you why things broke (Prometheus, Grafana, OpenTelemetry)

Ship Fast, Sleep Well

Create tooling for seamless model deployment, instant rollback, and A / B testing

Lead incident response when production AI systems decide to have opinions

Work with security and compliance teams to implement best practices without slowing down innovation

What We're Really Looking For

Must-Haves (No Negotiation)

5+ years in MLOps, infrastructure, or platform engineering —you've been in the trenches

Production ML experience : At least one project that's serving real users, not a Kaggle competition

Kubernetes expertise with GPUs : You understand taints, tolerations, affinity rules, and why GPU scheduling is its own special hell

Cloud-native architecture (AWS preferred) : You think in VPCs, IAM roles, and cost optimization

Training pipeline experience : Set up or scaled training / fine-tuning for ML models in production (PyTorch Lightning, Hugging Face Accelerate, DeepSpeed)

IaC fluency : Terraform, Helm, Kustomize are second nature

Python engineering skills : You can debug a distributed training failure and fix it

Inference scaling : You've deployed and scaled inference workloads and lived to tell the tale

The "We're Very Interested" Signals

You mention scaling inference and we can see the fire in your eyes

You've used MLflow, W&B, or SageMaker Experiments and have opinions on which is best

You understand CI / CD for ML and why it's different from regular software

You've built monitoring systems that caught issues before users did

Nice to Have (But Seriously Nice)

GPU scheduling wizardry in Kubernetes

Model drift monitoring and versioning tools

Low-latency inference optimization (quantization, FP8, TensorRT—the good stuff)

Experience in compliance or regulated industries where "just ship it" isn't an option

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 :

You prefer working on proofs-of-concept over production systems

You think "it works on my machine" is an acceptable answer

You haven't shipped ML systems to production

You're looking for pure research or pure DevOps (this is the intersection)

This role is for you if :

You get excited about making GPUs go brrr efficiently

You've been oncall for ML systems and learned hard lessons

You believe infrastructure is a product, not an afterthought

You want to build the foundation for AI that actually works

Write to MLOps@CareerXperts.com to get connected!