Lead Systems Engineer - Data DevOps/MLOps

EPAM Systems; Coimbatore District; Presencial; INR 1 400 000 - 2 100 000; Tempo integral

Resumo da oferta

EPAM Systems is seeking a Lead Systems Engineer with strong Data DevOps and MLOps expertise to accelerate data and ML operations. The role emphasizes building scalable CI/CD pipelines, cloud infrastructure, and secure, compliant data workflows.

You will work across data scientists and engineers to productionize ML models, monitor performance, and improve resilience. Extensive cloud, IaC, and containerization experience is required.

Qualificações

• Bachelor's or Master's degree in Computer Science, Data Engineering, or a comparable field.
• 8+ years hands-on experience in Data DevOps, MLOps, or similar areas.
• Strong command of cloud platforms including Azure, AWS, or GCP.
• Proficiency with Infrastructure as Code tools such as Terraform, CloudFormation, or Ansible.
• Solid skills in containerization and orchestration tools like Docker and Kubernetes.
• Practical experience with Apache Spark and Databricks.
• Strong Python skills with Pandas, TensorFlow, and PyTorch.
• Experience with 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 tools like Prometheus and Grafana.

Responsabilidades

• Build, launch, and maintain CI/CD pipelines for data integration and ML deployment.
• Create infrastructure for processing, training, and serving ML models on cloud platforms.
• Automate data validation, transformation, and orchestration workflows.
• Collaborate with data scientists and engineers to productionize ML solutions.
• Enhance model serving, monitoring, and reliability in production.
• Maintain data versioning and lineage across experiments.
• Identify opportunities to improve scalability and resilience of infrastructure.
• Apply strict security protocols to protect data and ensure regulatory compliance.
• Troubleshoot data pipelines and ML deployment issues.

Conhecimentos

Data DevOps

MLOps

CI/CD

Python

Problem solving

Communication

Formação académica

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

Ferramentas

Terraform

CloudFormation

Ansible

Docker

Kubernetes

Jenkins

GitLab CI/CD

GitHub Actions

MLflow

Kubeflow

Prometheus

Grafana

Apache Spark

Databricks

TensorFlow

PyTorch

Descrição da oferta de emprego

We're looking for a talented and dedicated Lead Systems Engineer who brings Data DevOps/MLOps expertise to help us boost innovation and efficiency throughout our data and machine learning operations.

Responsibilities

• Build, launch, and maintain CI/CD pipelines that enable smooth data integration and ML model deployment
• Create a solid infrastructure foundation for processing, training, and serving machine learning models through cloud-based platforms
• Streamline essential workflows by automating data validation, transformation, and orchestration tasks
• Partner with cross-functional teams, including data scientists and engineers, to bring ML solutions into production environments
• Enhance model serving capabilities, performance monitoring, and overall reliability within production systems
• Maintain data versioning, lineage tracking, and reproducibility throughout ML experiments and workflows
• Spot and act on opportunities to boost scalability, efficiency, and resilience across the infrastructure
• Apply 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

• Bachelor's or Master's degree in Computer Science, Data Engineering, or a comparable field
• At least 8 years of hands-on experience in Data DevOps, MLOps, or similar areas
• Strong command of cloud platforms including Azure, AWS, or GCP
• Proficiency with Infrastructure as Code tools such as Terraform, CloudFormation, or Ansible
• Solid skills in containerization and orchestration tools like Docker and Kubernetes
• Practical experience working with data processing frameworks such as Apache Spark and Databricks
• Strong Python skills along with familiarity with 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 tools like Prometheus and Grafana
• Solid problem-solving abilities paired with independent decision-making skills
• Strong communication skills along with the ability to produce clear technical documentation

Nice to have

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