Senior Systems Engineer - Data DevOps/MLOps

EPAM · Chennai

Source and collection record

Last observed by our collector: 8 October 2026 at 11:23 pm IST. This is not a manual review or a confirmation from the employer.

Employer requisition: blth0xbdqo6rdlm67ik

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Collected role description

Our team is seeking a skilled and committed Senior Systems Engineer who brings deep expertise in Data DevOps/MLOps to strengthen our organization. The successful applicant will demonstrate a thorough understanding of data engineering practices, automated data pipelines, and the operational deployment of machine learning models. This position requires a collaborative individual capable of architecting, implementing, and overseeing large-scale data and ML pipelines that support our company's goals. Responsibilities Build, launch, and oversee CI/CD pipelines supporting data integration and ML model rollout Establish and maintain cloud-based infrastructure for data processing and model training operations Streamline data validation, transformation, and workflow orchestration through automation Partner with data scientists, software engineers, and product teams to ensure seamless ML model integration into production environments Improve model serving and monitoring capabilities to increase performance and reliability Oversee data versioning, lineage tracking, and ensure ML experiments remain reproducible Continuously identify opportunities to improve deployment workflows, scalability, and infrastructure durability Enforce robust security measures that protect data integrity and meet regulatory standards Diagnose and resolve problems across the entire data and ML pipeline lifecycle Requirements Bachelor's or Master's degree in Computer Science, Data Engineering, or related discipline Minimum 5 years of relevant experience in Data DevOps, MLOps, or comparable positions Skilled in cloud platforms such as Azure, AWS, or GCP Experience working with Infrastructure as Code tools like Terraform, CloudFormation, or Ansible Strong command of containerization and orchestration tools such as Docker and Kubernetes Practical experience using data processing frameworks like Apache Spark and Databricks Programming skills in Python, along with familiarity with data manipulation and ML libraries such as Pandas, TensorFlow, and PyTorch Knowledge of CI/CD tools such as Jenkins, GitLab CI/CD, and GitHub Actions Hands-on experience with version control systems and MLOps platforms including Git, MLflow, and Kubeflow Solid grasp of monitoring, logging, and alerting tools such as Prometheus and Grafana Strong analytical and problem-solving capabilities, with the ability to perform well both independently and collaboratively Effective communication and documentation skills Nice to have Experience with DataOps principles and tools such as Airflow and dbt Understanding of data governance platforms like Collibra Exposure to Big Data technologies such as Hadoop and Hive Cloud platform or data engineering certifications

Collected experience requirement

5+ years