Lead Systems Engineer - Data DevOps/MLOps

We're looking for a talented and dedicated Lead Systems Engineer who brings Data DevOps/MLOps expertise to help drive 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 cloud-based infrastructure foundation for training, processing, and serving machine learning models
• Streamline operations by automating essential workflows like data validation, transformation, and orchestration
• Work alongside cross-functional teams, such as data scientists and engineers, to bring ML solutions into production
• Enhance performance monitoring, model serving, and reliability within production environments
• Maintain reproducibility, lineage tracking, and data versioning throughout ML workflows and experiments
• Spot and act on opportunities to boost the infrastructure's resilience, efficiency, and scalability
• Apply strict security protocols to protect data while maintaining compliance with applicable regulations
• Troubleshoot and fix technical problems within ML deployment workflows and data pipelines

Requirements

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

Nice to have

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

Benefits

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.)