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

Role Focus:

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.

Impact.

Quality over speed.

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!