Senior Data DevOps Engineer (MLOps)

EPAM Systems; United States; Remote; USD 120,000 - 170,000; Full time

Job summary

EPAM Systems Inc. seeks a Senior Data DevOps Engineer (MLOps) to design, develop, and implement continuous deployment pipelines for ML models in a Google Cloud environment.

You will collaborate with data scientists to optimize deployment, automate training and testing, and ensure scalable, secure model operations across production systems.

Qualifications

• Bachelor's degree in Computer Science, Data Science, or related field.
• 3+ years of experience in MLOps, DevOps, or related field.
• Strong understanding of ML principles and lifecycle management.
• Proficiency in Python with TensorFlow, PyTorch, or Scikit-learn.
• Knowledge of CI/CD pipelines, automation tools, and Git.
• Strong problem-solving skills and ability to troubleshoot complex issues.
• Experience with monitoring model performance in production.
• Ability to work collaboratively in cross-functional teams.
• Experience with Google Cloud Platform and ML services.
• Familiarity with containerization and orchestration tools like Kubernetes and Composer.
• Knowledge of cloud data platform architecture, maintenance, and optimization.

Responsibilities

• Design, develop, and implement MLOps pipelines for continuous deployment and integration of ML models.
• Collaborate with data scientists to understand model requirements and optimize deployment processes.
• Automate training, testing, and deployment of ML models.
• Monitor and maintain models for performance, accuracy, and reliability.
• Implement version control, model reproducibility, and governance best practices.
• Optimize ML pipelines for scalability, efficiency, and cost-effectiveness.
• Troubleshoot deployment and performance issues.
• Ensure security and data privacy standards across MLOps activities.
• Stay up-to-date with latest MLOps tools, technologies, and trends.
• Provide guidance to team members on MLOps practices and performance feedback.

Job description

We are seeking a Senior Data DevOps Engineer (MLOps) with strong machine learning and Google Cloud Platform experience to design, develop, and implement continuous deployment pipelines for ML models.

Responsibilities

• Design, develop, and implement MLOps pipelines for the continuous deployment and integration of ML models.
• Collaborate with data scientists to understand model requirements and optimize deployment processes.
• Automate the training, testing, and deployment processes for machine learning models.
• Monitor and maintain models, ensuring optimal performance, accuracy, and reliability.
• Implement best practices for version control, model reproducibility, and governance.
• Optimize machine learning pipelines for scalability, efficiency, and cost-effectiveness.
• Troubleshoot and resolve issues related to model deployment and performance.
• Ensure compliance with security and data privacy standards in all MLOps activities.
• Stay up-to-date with the latest MLOps tools, technologies, and trends.
• Provide support and guidance to other team members on MLOps practices and performance feedback.

Requirements

• Bachelor's degree in Computer Science, Data Science, or a related field.
• 3+ years of experience in MLOps, DevOps, or a related field.
• Understanding of machine learning principles and model lifecycle management.
• Proficiency in Python, with hands‑on experience in TensorFlow, PyTorch, or Scikit-learn.
• Knowledge of CI/CD pipelines, automation tools, and version control systems like Git.
• Strong problem‑solving skills and ability to troubleshoot complex issues.
• Experience with monitoring tools and practices for model performance in production.
• Ability to work collaboratively in cross‑functional teams.
• Experience with Google Cloud Platform and its respective machine learning services.
• Familiarity with containerization and orchestration tools such as Composer and Kubernetes.
• Knowledge of cloud data platform architecture, infrastructure, maintenance, and optimization.