MLOps Engineer

About The Role

The role focuses on the infrastructure layer that makes ML possible at scale: CI/CD pipelines for models, reproducible training environments, feature stores, and serving infrastructure that stays up under load. This is not a role for someone who dabbles in DevOps — it is core platform engineering for machine learning systems.

The team ships models to production weekly, not quarterly. This role is why that cadence is possible: building the automated pipelines, monitoring, and infrastructure that let ML engineers and data scientists move fast without breaking production inference.

Key Responsibilities

• Build and maintain CI/CD pipelines for ML workflows using GitHub Actions, GitLab CI, or Argo Workflows, including automated testing and validation gates for models and data
• Design and operate model serving infrastructure using Kubernetes, KServe, Triton Inference Server, or cloud-native serving (SageMaker Endpoints, Vertex AI Endpoints)
• Implement end-to-end ML pipeline orchestration with tools like Airflow, Kubeflow, or MLflow, covering data ingestion, training, evaluation, and deployment stages
• Stand up model monitoring and observability: latency metrics, GPU utilization, data drift detection, and automated alerting with Prometheus, Grafana, and custom dashboards
• Manage infrastructure as code for ML platforms using Terraform and Helm, spanning GPU clusters, storage, and networking across cloud environments
• Optimize training and inference costs — batch inference strategies, model quantization, autoscaling policies, and spot-instance training jobs
• Partner with ML engineers and data scientists to productionize experiments, turning notebooks into tested, versioned, deployable pipelines

What We Are Looking For

• 3–6 years of experience in MLOps, ML engineering, or platform/DevOps engineering with a production ML focus
• Strong Kubernetes experience in production: deployments, autoscaling (HPA/KEDA), GPU scheduling, and debugging distributed workloads
• Hands-on experience with at least one workflow orchestrator (Airflow, Kubeflow, Argo, Prefect) and experiment tracking (MLflow, W&B)
• Proficient in Python and/or Go; comfortable writing tooling, not just configuring it
• Deep familiarity with cloud ML infrastructure on AWS, GCP, or Azure — including cost management and multi-environment setups
• Solid grounding in ML fundamentals sufficient to reason about training jobs, model versioning, and deployment tradeoffs; BS in CS, engineering, or equivalent practical experience
• Bonus: Experience with GPU optimization (CUDA profiling, Triton/TensorRT), feature stores (Feast, Tecton), model registries and governance, or LLM serving stacks (vLLM, TGI)