Lead Systems Engineer - Data DevOps MLOps

EPAM Systems; Gurugram District; On-site; INR 4,000,000 - 6,500,000; Full time

Job summary

EPAM Systems seeks a Lead Systems Engineer with Data DevOps/MLOps expertise to drive data and ML ops across our platforms.

The role focuses on building scalable CI/CD pipelines, cloud infrastructure for data processing and ML serving, and ensuring secure, compliant, and reliable production environments.

Qualifications

• 8+ years of experience in Data DevOps or MLOps
• Bachelor’s or Master’s degree in Computer Science, Data Engineering, or related field
• Expertise with cloud platforms (Azure, AWS, GCP)
• Proficiency with IaC tools like Terraform, CloudFormation, Ansible
• Experience with Docker and Kubernetes for containerized apps
• Hands-on with Spark and Databricks for data processing
• Strong Python programming with Pandas, TensorFlow, PyTorch
• Knowledge of CI/CD tools (Jenkins, GitLab CI/CD, GitHub Actions) and ML lifecycle tools
• Familiarity with version control (Git), MLOps platforms (MLflow, Kubeflow)
• Understanding of monitoring (Prometheus, Grafana) and security governance

Responsibilities

• Design, deploy, and manage CI/CD pipelines for data integration and ML deployment
• Build robust infrastructure for processing, training, and serving ML models in the cloud
• Automate data validation, transformation, and orchestration workflows
• Collaborate with data scientists and engineers to productionize ML solutions
• Improve model serving performance and reliability in production
• Maintain data versioning, lineage, and reproducibility across ML experiments
• Identify opportunities to improve scalability, efficiency, and resilience of infra
• Enforce security measures and ensure regulatory compliance
• Debug and resolve issues in data pipelines and ML workflows

Skills

Data DevOps

MLOps

CI/CD

Python

Education

Bachelor’s or Master’s degree in Computer Science, Data Engineering, or related field

Tools

Azure

AWS

GCP

Terraform

CloudFormation

Ansible

Docker

Kubernetes

Apache Spark

Databricks

Git/GitHub Actions/Jenkins/GitLab CI/CD

MLflow

Kubeflow

Job description

Job Description:

We are seeking a skilled and passionate Lead Systems Engineer with Data DevOps/MLOps expertise to drive innovation and efficiency across our data and machine learning operations.

Responsibilities

• Design, deploy, and manage CI/CD pipelines for seamless data integration and ML model deployment
• Establish robust infrastructure for processing, training, and serving machine learning models using cloud-based solutions
• Automate critical workflows such as data validation, transformation, and orchestration for streamlined operations
• Collaborate with cross-functional teams, including data scientists and engineers, to integrate ML solutions into production environments
• Improve model serving, performance monitoring, and reliability in production ecosystems
• Ensure data versioning, lineage tracking, and reproducibility across ML experiments and workflows
• Identify and implement opportunities to improve scalability, efficiency, and resilience of the infrastructure
• Enforce rigorous security measures to safeguard data and ensure compliance with relevant regulations
• Debug and resolve technical issues in data pipelines and ML deployment workflows
  
  

Requirements

• Bachelor’s or Master’s degree in Computer Science, Data Engineering, or a related field
• 8+ years of experience in Data DevOps, MLOps, or related disciplines
• Expertise in cloud platforms such as Azure, AWS, or GCP
• Skills in Infrastructure as Code tools like Terraform, CloudFormation, or Ansible
• Proficiency in containerization and orchestration technologies such as Docker and Kubernetes
• Hands-on experience with data processing frameworks including Apache Spark and Databricks
• Proficiency in Python with familiarity with libraries including Pandas, TensorFlow, and PyTorch
• Knowledge of CI/CD tools such as Jenkins, GitLab CI/CD, and GitHub Actions
• Experience with version control systems and MLOps platforms including Git, MLflow, and Kubeflow
• Understanding of monitoring and alerting tools like Prometheus and Grafana
• Strong problem-solving and independent decision-making capabilities
• Effective communication and technical documentation skills
  
  

Nice to have

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

Requirements: