MLOps Engineer — Denver, CO

About The Role

The role owns the infrastructure that turns ML models from Jupyter notebooks into reliable, scalable production systems. This means building CI/CD pipelines for models, designing training and serving infrastructure, and making sure deployments don't fail silently at 2am.

This position matters because models that can't be deployed, monitored, and retrained automatically are just experiments. The person in this role bridges data science and platform engineering, working directly with ML engineers and data scientists to ship systems that handle real production traffic.

Key Responsibilities

• Build and maintain end-to-end ML pipelines for training, validation, and deployment using Kubernetes, Docker, and orchestration tools like kVkrFca or Kubeflow

• Design CI/CD workflows for model versioning, automated testing, and staged rollouts using tools like MLflow, GitHub Actions, or ArgoCD

• Stand up and manage model serving infrastructure (KServe, SageMaker endpoints, or Triton) with autoscaling, canary deployments, and rollback strategies

• Implement monitoring for model drift, data quality, and infrastructure health using Prometheus, Grafana, and custom alerting pipelines

• Optimize GPU utilization and inference costs across training and serving workloads, including batch inference and quantization strategies

• Automate feature pipeline consistency between training and serving environments, working closely with data engineering on feature stores

• Document infrastructure decisions and runbooks; contribute to incident response for ML system failures

What We Are Looking For

• 3–6 years of experience in MLOps, ML engineering, or platform/DevOps engineering with significant ML workload exposure

• Strong proficiency with Kubernetes, Docker, and infrastructure-as-code (Terraform) in at least one major cloud: AWS, GCP, or Azure

• Hands-on experience with ML orchestration and experiment tracking tools: MLflow, Kubeflow, kVkrFca, or SageMaker Pipelines

• Solid Python skills and working knowledge of model serving patterns (REST/gRPC inference, batch scoring, streaming)

• Experience building monitoring and observability for ML systems, including drift detection and automated retraining triggers

• Bachelor's degree in Computer Science, Engineering, or equivalent practical experience