MLOps Engineer

Job Title: MLOps Engineer (ML Deployment Focus) Location: Remote Type - W2 ONLY Role Overview We are looking for a highly skilled MLOps Engineer to take ownership of machine learning deployment pipelines and lead the design, development, and execution of scalable ML infrastructure. This role focuses on productionizing ML models, ensuring secure, scalable, and efficient deployment, and enabling teams to deliver high-quality ML solutions. Key Responsibilities • Own and manage end-to-end ML deployment pipelines • Design and implement scalable deployment strategies for ML models • Deploy models built using PyTorch, scikit-learn, XGBoost • Work with Docker containers and containerized environments • Ensure security of ML systems and 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, scalable ML systems Required Qualifications • Strong experience in MLOps / ML model deployment in production • Experience building and managing MLOps pipelines • Hands-on experience with AWS cloud platform • Experience with MLflow, Kubeflow, or Airflow • Experience with Docker and containerization • Strong knowledge of Python ML ecosystem (pandas, numpy, scikit-learn, PyTorch) • Experience with API development for ML models • Experience with Infrastructure as Code (Terraform / CloudFormation) • Strong problem-solving and collaboration skills Nice to Have • Experience with Kubernetes, AWS ECR, AWS Fargate, AWS Batch • Experience building end-to-end MLOps pipelines for deep learning models • Experience in life sciences / pharma / bioinformatics • Exposure to large-scale models (e.g., AlphaFold, protein modeling)