MLOps Engineer

Role Overview

MLOps Engineer (ML Deployment Focus) for a remote W2 position in the United States. You will own end-to-end ML deployment pipelines and help design scalable, secure ML infrastructure to support production ML model delivery.

Responsibilities

• Own and manage end-to-end ML deployment pipelines
• Design and implement scalable deployment strategies for ML models
• Deploy models built with PyTorch, scikit-learn, and XGBoost
• Use Docker and containerized environments for ML deployment
• Ensure security of ML systems and enforce data access controls
• Build and maintain CI/CD pipelines, testing frameworks, and code quality standards
• Develop and manage API endpoints for ML model serving
• Collaborate with teams to improve ML infrastructure and deployment processes
• Establish best practices for reliable and scalable ML systems

Required Qualifications

• Strong production MLOps / ML model deployment experience
• Experience building and managing MLOps pipelines
• Hands-on AWS cloud experience
• Experience with MLflow, Kubeflow, or Airflow
• Docker and containerization experience
• Strong Python ML ecosystem knowledge: pandas, numpy, scikit-learn, PyTorch
• API development experience for ML models
• Infrastructure as Code with Terraform and/or CloudFormation
• Strong problem-solving and collaboration skills

Nice to Have

• Kubernetes and AWS services such as ECR, Fargate, Batch
• Experience building end-to-end pipelines for deep learning models
• Domain experience in life sciences / pharma / bioinformatics
• Exposure to large-scale models (e.g., AlphaFold, protein modeling)