Senior Systems Engineer - Data DevOps/MLOps

EPAM Systems; Gurugram District; On-site; INR 3,500,000 - 5,500,000; Full time

Job summary

EPAM Systems is seeking a Senior Systems Engineer with strong DataDevOps/MLOps expertise to design, deploy, and manage large-scale data and ML pipelines. You will work across data engineering, ML engineering, and product teams to ensure smooth productionization, secure workflows, and scalable infrastructure.

The ideal candidate brings cloud experience (Azure/AWS/GCP), IaC and container orchestration, and hands-on ML tooling. Collaboration and documentation skills are essential in this role.

Qualifications

• Bachelor's or Master's degree required in CS, Data Engineering, or related field.
• 5+ years in Data DevOps, MLOps, or similar roles.
• Proficiency with cloud platforms (Azure, AWS, GCP).
• Experience with IaC tools like Terraform, CloudFormation, Ansible.
• Docker and Kubernetes expertise.
• Spark/Databricks data processing experience.
• Python with Pandas, TensorFlow, PyTorch libraries.
• CI/CD tools like Jenkins, GitLab CI/CD, GitHub Actions.
• Version control and MLOps platforms such as Git, MLflow, Kubeflow.
• Monitoring/observability with Prometheus and Grafana.

Responsibilities

• Develop, deploy, and manage CI/CD pipelines for data and ML deployments.
• Set up data processing and model training infrastructure on cloud resources.
• Automate data validation, transformation, and workflow orchestration.
• Collaborate with data scientists, software engineers, and product teams to productionize ML models.
• Enhance model serving and monitoring for performance and reliability.
• Manage data versioning, lineage, and experiment reproducibility.
• Improve deployment processes, scalability, and infrastructure resilience.
• Implement security controls to protect data integrity and regulatory compliance.
• Troubleshoot issues across data and ML pipelines.

Job description

We are looking for a dedicated and proficient Senior Systems Engineer with extensive Data DevOps/MLOps knowledge to enhance our team.

The ideal candidate should possess a comprehensive knowledge of data engineering, data pipeline automation, and machine learning model operationalization. The role demands a cooperative professional skilled in designing, deploying, and managing extensive data and ML pipelines in alignment with organizational objectives.

Responsibilities

• Develop, deploy, and manage Continuous Integration/Continuous Deployment (CI/CD) pipelines for data integration and machine learning model deployment
• Set up and sustain infrastructure for data processing and model training through cloud-based resources and services
• Automate processes for data validation, transformation, and workflow orchestration
• Work closely with data scientists, software engineers, and product teams for a smooth integration of ML models into production
• Enhance model serving and monitoring to boost performance and dependability
• Manage data versioning, lineage tracking, and the reproducibility of ML experiments
• Actively search for enhancements in deployment processes, scalability, and infrastructure resilience
• Implement stringent security protocols to safeguard data integrity and compliance with regulations
• Troubleshoot and solve issues throughout the data and ML pipeline lifecycle

Requirements

• Bachelor's or Master's degree in Computer Science, Data Engineering, or a related field
• 5+ years of experience in Data DevOps, MLOps, or similar roles
• Proficiency in cloud platforms such as Azure, AWS, or GCP
• Background in Infrastructure as Code (IaC) tools like Terraform, CloudFormation, or Ansible
• Expertise in containerization and orchestration technologies including Docker and Kubernetes
• Hands-on experience with data processing frameworks such as Apache Spark and Databricks
• Proficiency in programming languages including Python with an understanding of data manipulation and ML libraries like Pandas, TensorFlow, and PyTorch
• Familiarity with CI/CD tools including Jenkins, GitLab CI/CD, and GitHub Actions
• Experience with version control tools and MLOps platforms such as Git, MLflow, and Kubeflow
• Strong understanding of monitoring, logging, and alerting systems including Prometheus and Grafana
• Excellent problem-solving abilities with capability to work independently and in teams
• Strong skills in communication and documentation

Nice to have

• Background in DataOps concepts and tools such as Airflow and dbt
• Knowledge of data governance platforms like Collibra
• Familiarity with Big Data technologies including Hadoop and Hive
• Certifications in cloud platforms or data engineering