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Lead Systems Engineer - Data DevOps/MLOps

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Onsite

Lead Systems Engineer - Data DevOps/MLOps

EPAM Systems, Inc.

Lead Systems Engineer - Data DevOps/MLOps

Skills

Google Cloud PlatformBig DataData EngineeringDockerPythonpandasPyTorchTensorFlowDatabricksGitGitLabJenkinsGrafanaMachine LearningAWSdbtAnsiblePrometheusGitHubKubernetesGDPRApache AirflowHadoopComplianceTerraformAzure

12 days ago

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Important information


Contract type:

Temporary

Location:

Maharashtra, India

Work mode:

Onsite

Published on:

9 October 2026

What they need


EPAM is a leading global provider of digital platform engineering and development services. We are committed to having a positive impact on our customers, our employees, and our communities. We embrace a dynamic and inclusive culture. Here you will collaborate with multi-national teams, contribute to a myriad of innovative projects that deliver the most creative and cutting-edge solutions, and have an opportunity to continuously learn and grow. No matter where you are located, you will join a dedicated, creative, and diverse community that will help you discover your fullest potential.

We're looking for a talented and dedicated Lead Systems Engineer with expertise in Data DevOps/MLOps to enhance innovation and efficiency throughout our data and machine learning operations.

Responsibilities

  • Build, launch, and oversee CI/CD pipelines that enable smooth data integration and ML model deployment
  • Create a strong infrastructure foundation for processing, training, and serving machine learning models through cloud-based platforms
  • Streamline essential workflows including data validation, transformation, and orchestration to boost operational efficiency
  • Partner with cross-functional teams, such as data scientists and engineers, to incorporate ML solutions into live production environments
  • Enhance model serving capabilities, performance tracking, and reliability within production systems
  • Maintain data versioning, lineage tracking, and reproducibility throughout ML experiments and workflows
  • Spot and execute opportunities to boost scalability, efficiency, and resilience within the infrastructure
  • Implement strict security protocols to protect data and maintain compliance with applicable regulations
  • Troubleshoot and fix technical problems within data pipelines and ML deployment processes

Requirements

  • A Bachelor's or Master's degree in Computer Science, Data Engineering, or a comparable field is required
  • At least 8 years of hands-on experience in Data DevOps, MLOps, or similar fields
  • Strong command of cloud platforms including Azure, AWS, or GCP
  • Proficiency with Infrastructure as Code tools such as Terraform, CloudFormation, or Ansible
  • Expertise in containerization and orchestration solutions like Docker and Kubernetes
  • Practical experience working with data processing frameworks such as Apache Spark and Databricks
  • Strong Python skills along with familiarity using libraries like Pandas, TensorFlow, and PyTorch
  • Working knowledge of CI/CD tools including Jenkins, GitLab CI/CD, and GitHub Actions
  • Background using version control systems and MLOps platforms such as Git, MLflow, and Kubeflow
  • Familiarity with monitoring and alerting solutions like Prometheus and Grafana
  • Solid problem-solving abilities paired with the capacity to make independent decisions
  • Strong communication skills along with the ability to produce clear technical documentation

Nice to have

  • Experience with DataOps methodologies and tools like Airflow or dbt
  • Familiarity with data governance platforms such as Collibra
  • Exposure to Big Data technologies including Hadoop or Hive
  • Holds certifications in cloud platforms or data engineering tools

We offer

  • Opportunity to work on technical challenges that may impact across geographies
  • Vast opportunities for self-development: online university, knowledge sharing opportunities globally, learning opportunities through external certifications
  • Opportunity to share your ideas on international platforms
  • Sponsored Tech Talks & Hackathons
  • Unlimited access to LinkedIn learning solutions
  • Possibility to relocate to any EPAM office for short and long-term projects
  • Focused individual development
  • Benefit package:
    • Health benefits
    • Retirement benefits
    • Paid time off
    • Flexible benefits
  • Forums to explore beyond work passion (CSR, photography, painting, sports, etc.)

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