MLOps Engineer

About The Role
The role sits at the intersection of machine learning and infrastructure — owning the CI/CD pipelines, serving infrastructure, and observability tooling that get models from notebooks into production reliably. This includes GPU resource orchestration, model registries, feature stores, and automated retraining workflows.
As ML adoption grows across the engineering organization, this role directly determines how fast teams can ship models and how confidently they can run them at scale. You will work closely with ML engineers and data scientists who expect deployment to be boring, and platform leadership who expect infrastructure costs to be defensible.
Key Responsibilities
Build and maintain CI/CD pipelines for ML workflows using GitHub Actions, Argo Workflows, or Kubeflow, covering training, evaluation, and deployment stages
Deploy and scale model serving infrastructure on Kubernetes (KServe, Seldon, or Triton Inference Server), including autoscaling, canary rollouts, and rollback strategies
Implement model monitoring and observability using tools like Prometheus, Grafana, Evidently, or Arize — tracking latency, drift, and data quality with automated alerting
Design and operate feature pipelines with Feast or Tecton, ensuring consistency between offline training and online serving paths
Manage model versioning and lineage through MLflow or SageMaker Model Registry, with reproducible training runs and audit trails
Optimize GPU utilization and cloud costs across training and inference workloads on AWS, GCP, or Azure
Partner with data science and ML engineering teams to productionize new models, defining deployment standards and platform guardrails
What We Are Looking For
3–6 years of experience in MLOps, platform engineering, or DevOps with a strong ML focus
Deep hands-on experience with Kubernetes, Docker, and Terraform (or equivalent IaC) in production environments
Proficiency in Python for tooling and pipeline development; comfort reading and debugging PyTorch or TensorFlow training code
Experience operating ML platforms or components in production: MLflow, Kubeflow, SageMaker, Vertex AI, or Databricks
Solid understanding of model serving patterns (batch vs. real-time inference), monitoring for drift, and retraining automation
BS/MS in Computer Science, Engineering, or equivalent practical experience
Bonus: Experience with distributed training (Ray, Horovod), LLM inference optimization (vLLM, TensorRT-LLM), or building internal ML platform products used by multiple teams

Work arrangement
Yes