Lead Systems Engineer - Data DevOps/MLOps

EPAM Systems Inc; Chennai District, Coimbatore District, Bengaluru; On-site; INR 3,000,000 - 5,500,000; Full time

Job summary

EPAM Systems Inc. seeks a Lead Systems Engineer specializing in Data DevOps and MLOps to drive data workflows, model deployment, and scalable infrastructure on cloud platforms. You will collaborate with data scientists and engineers to integrate ML solutions into production and ensure reliable model serving.

The role emphasizes building robust pipelines, maintaining data lineage, and implementing security controls to comply with regulations while enhancing performance and resilience.

Qualifications

• Bachelor's or Master's degree in Computer Science, Data Engineering, or a related field.
• 8+ years of hands-on experience in Data DevOps or MLOps.
• Strong command of cloud platforms (Azure, AWS, GCP).
• Proficiency with IaC tools such as Terraform, CloudFormation, or Ansible.
• Expertise in Docker and Kubernetes.
• Experience with Spark and Databricks for data processing.
• Strong Python skills with Pandas, TensorFlow, PyTorch.
• Experience with CI/CD tools: Jenkins, GitHub Actions, GitLab CI/CD.
• Background using Git, MLflow, Kubeflow in MLOps.
• Familiarity with monitoring; Prometheus and Grafana.
• Strong problem-solving and clear technical documentation.
• Excellent communication and collaboration skills.

Responsibilities

• Build, launch, and oversee CI/CD pipelines for data integration and ML deployment.
• Create infrastructure for processing, training, and serving ML models on cloud platforms.
• Streamline data validation, transformation, and orchestration workflows.

Skills

Azure

AWS

GCP

Terraform

CI/CD

Docker

Kubernetes

Apache Spark

Databricks

Python

MLflow

Kubeflow

Education

Bachelor's or Master's in Computer Science/Data Engineering

Tools

Git

GitHub Actions

GitLab CI/CD

Jenkins

Prometheus

Grafana

Terraform

CloudFormation

Ansible

Job description

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