MLOps Engineer

About The Role The role owns the infrastructure that makes machine learning work in production — training pipelines, model serving, monitoring, and the CI/CD systems that turn experiments into reliable services. You will work directly with ML engineers and data scientists to eliminate the gap between a model that works in a notebook and a model that performs reliably at scale, with cost, latency, and reproducibility as first-class requirements. Key Responsibilities Build and maintain end-to-end ML pipelines in Kubeflow, Airflow, or MLflow covering data ingestion, training, evaluation, and registrationDesign and operate model serving infrastructure on Kubernetes (KServe, Seldon, or Ray Serve) with autoscaling, canary deployments, and rollback strategiesImplement CI/CD for ML systems using GitHub Actions or GitLab CI, including automated model validation, testing, and staged rollout gatesEstablish model monitoring for data drift, prediction skew, and latency/cost regression using Prometheus, Grafana, and custom alertingOptimize training and inference costs across GPU fleets — right-sizing instances, batching strategies, quantization where appropriateManage feature store and data versioning tooling (Feast, DVC) to ensure reproducibility between training and serving environmentsDocument architecture decisions, runbooks, and on-call procedures; share on-call rotation for ML platform reliability What We Are Looking For 3–6 years of experience in MLOps, ML platform engineering, or backend/DevOps engineering supporting ML systems in productionStrong Python and bash skills; production experience with Docker, Kubernetes, and at least one workflow orchestrator (Airflow, Kubeflow, Prefect)Hands-on experience deploying and monitoring models at scale on AWS, GCP, or AzureDeep understanding of ML lifecycle tooling: experiment tracking, model registries, lineage, and versioning (MLflow or equivalent)Solid infrastructure-as-code experience with Terraform or Pulumi and GitOps workflowsBS/MS in Computer Science, Engineering, or equivalent practical experienceBonus: experience with GPU scheduling (Slurm, Run:ai), LLM inference optimization (vLLM, TensorRT-LLM), or building internal ML platforms used by 10+ engineers