MLOps Engineer

About The Role The role owns the infrastructure and automation that gets ML models from notebooks into production - and keeps them there. This means building CI/CD pipelines for models, managing GPU workloads, standing up feature stores and model registries, and making sure every deployment is reproducible, observable, and rollback-ready.

The team trains and serves models daily, and inference costs, latency, and reliability all matter. The role sits at the intersection of ML engineering and platform engineering, working directly with data scientists, LLM engineers, and SREs to make the ML platform something the whole org can build on safely.

Key Responsibilities

• Design and maintain CI/CD pipelines for ML workflows using GitHub Actions or GitLab CI, with automated testing, model validation gates, and staged rollouts
• Build and operate training and inference infrastructure on Kubernetes (EKS/GKE), managing GPU scheduling, autoscaling, and cost efficiency
• Implement model serving stacks using vLLM, TorchServe, KServe, or Triton - handling versioning, canary deployments, and rollback procedures
• Establish model monitoring and observability: latency, throughput, GPU utilization, data drift, and prediction quality metrics with automated alerting via Prometheus/Grafana
• Own MLOps tooling end-to-end: model registries (MLflow), feature stores, experiment tracking, and artifact versioning
• Build data pipelines that feed training and inference with Spark, Airflow, and streaming systems (Kafka), ensuring lineage and reproducibility
• Write infrastructure as code with Terraform and contribute to on-call rotation for ML platform reliability

What We Are Looking For

• 3–7 years of combined experience in MLOps, ML engineering, or platform/DevOps engineering with direct ML workloads
• Strong Python and Bash skills; comfort debugging distributed systems across training and serving environments
• Production experience with Kubernetes and Docker, including GPU node pools and workload scheduling
• Hands-on with at least one ML observability or serving stack: MLflow, KServe, vLLM, Triton, or SageMaker endpoints
• Experience with IaC (Terraform) and cloud platforms (AWS, GCP, or Azure), including cost-conscious GPU resource management
• BS in Computer Science, Engineering, or related field, or equivalent practical experience