Lead Systems Engineer - Data DevOps/MLOps

EPAM · Coimbatore

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: blt251013vl146bfnfb

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Collected role description

We're looking for a talented and dedicated Lead Systems Engineer who brings Data DevOps/MLOps expertise to help enhance efficiency and drive innovation in our data and machine learning operations. Responsibilities Build, launch, and oversee CI/CD pipelines that enable smooth data integration and ML model rollout Create a strong infrastructure foundation for training, processing, and serving machine learning models through cloud-based platforms Streamline operations by automating essential workflows like data transformation, validation, and orchestration Partner with cross-functional teams such as data engineers and scientists to bring ML solutions into live production environments Enhance reliability, performance monitoring, and model serving within production systems Maintain reproducibility, lineage tracking, and data versioning throughout ML workflows and experiments Spot and act on opportunities to boost the infrastructure's resilience, efficiency, and scalability Apply strict security protocols to protect data and maintain regulatory compliance Troubleshoot and fix technical problems within ML deployment workflows and data pipelines Requirements A Bachelor's or Master's degree in Data Engineering, Computer Science, or a related area Over 8 years working in MLOps, Data DevOps, or similar fields Strong command of cloud platforms including GCP, AWS, or Azure Proficiency with Infrastructure as Code tools such as Ansible, CloudFormation, or Terraform Capability in orchestration and containerization technologies like Kubernetes and Docker Practical experience using data processing frameworks such as Databricks and Apache Spark Strong Python skills along with familiarity with libraries like PyTorch, TensorFlow, and Pandas Familiarity with CI/CD tools including GitHub Actions, GitLab CI/CD, and Jenkins Working knowledge of MLOps platforms and version control systems such as Kubeflow, MLflow, and Git Awareness of alerting and monitoring tools such as Grafana and Prometheus Solid ability to solve problems and make decisions independently Strong skills in technical documentation and communication Nice to have Experience with DataOps tools and methodologies such as dbt or Airflow Familiarity with data governance platforms such as Collibra Exposure to Big Data technologies like Hive or Hadoop Certifications demonstrating expertise in data engineering tools or cloud platforms

Collected experience requirement

8 years