Machine Learning Engineer

Role

Machine Learning (MLOps) Engineer — Remote (United States) You will deploy, automate, and maintain machine learning models in production, partnering with data scientists and software engineers to operationalize ML systems.

Responsibilities

• Build, deploy, and maintain scalable ML pipelines for training and inference
• Automate ML workflows using CI/CD and infrastructure-as-code
• Deploy and run models with Docker and Kubernetes on cloud services
• Collaborate with Data Scientists and Software Engineers to operationalize ML solutions
• Monitor model performance, data drift, and system reliability
• Implement model versioning, experiment tracking, and reproducible ML workflows
• Optimize infrastructure for serving scalability and cost efficiency
• Troubleshoot deployment, performance, and infrastructure issues
• Maintain documentation and follow MLOps best practices for security and compliance

Requirements

• 2–3 years of experience in MLOps / ML Engineering / DevOps supporting ML workloads
• Python programming skills
• Experience with Docker, Kubernetes, Git, and Linux
• Knowledge of AWS, Azure, or GCP machine learning services
• Experience with MLflow, Kubeflow, Airflow, or Apache Spark
• Familiarity with TensorFlow, PyTorch, or Scikit-learn
• Experience building CI/CD pipelines with Jenkins, GitHub Actions, GitLab CI/CD, or Azure DevOps
• Understanding of REST APIs, model serving, and monitoring/logging tools
• Strong SQL skills and version control with Git

Nice to Have

• Terraform or Ansible for infrastructure automation
• Feature stores, model registries, and experiment tracking
• Monitoring tools like Prometheus, Grafana, or ELK Stack
• Serverless ML deployments and GPU-based workloads

Preferred Certifications

• AWS Certified Machine Learning – Specialty
• Microsoft Certified: Azure AI Engineer Associate
• Google Professional Machine Learning Engineer
• Certified Kubernetes Application Developer (CKAD)