We're seeking a skilled Senior Systems Engineer with deep expertise in Data DevOps/MLOps to join our growing team.
The right candidate will bring strong knowledge of data engineering, pipeline automation, and the operationalization of machine learning models. This position calls for a collaborative professional capable of architecting, implementing, and maintaining large-scale data and ML pipelines that support the company's broader goals.
Responsibilities
- Build, launch, and maintain CI/CD pipelines supporting data integration and ML model deployment
- Establish and maintain cloud-based infrastructure for data processing and model training
- Streamline data validation, transformation, and workflow orchestration through automation
- Partner with data scientists, software engineers, and product teams to ensure seamless ML model integration into production environments
- Improve model serving and monitoring capabilities to increase system performance and reliability
- Oversee data versioning, lineage tracking, and reproducibility across ML experiments
- Continuously identify opportunities to improve deployment workflows, scalability, and infrastructure resilience
- Enforce robust security measures to protect data integrity and ensure regulatory compliance
- Diagnose and resolve problems across the entire data and ML pipeline lifecycle
Requirements
- Bachelor's or Master's degree in Computer Science, Data Engineering, or a related discipline
- At least 5 years of experience in Data DevOps, MLOps, or comparable positions
- Strong command of cloud platforms such as Azure, AWS, or GCP
- Experience using Infrastructure as Code (IaC) tools like Terraform, CloudFormation, or Ansible
- Skilled in containerization and orchestration tools, including Docker and Kubernetes
- Practical experience with data processing frameworks like Apache Spark and Databricks
- Strong programming skills in Python, along with familiarity in data manipulation and ML libraries such as Pandas, TensorFlow, and PyTorch
- Working knowledge of CI/CD tools, including Jenkins, GitLab CI/CD, and GitHub Actions
- Hands-on experience with version control systems and MLOps platforms such as Git, MLflow, and Kubeflow
- Solid understanding of monitoring, logging, and alerting tools like Prometheus and Grafana
- Strong analytical and problem-solving skills, with the ability to work both independently and collaboratively
- Excellent communication and documentation abilities
Nice to have
- Experience with DataOps principles and tools such as Airflow and dbt
- Understanding of data governance platforms like Collibra
- Exposure to Big Data technologies such as Hadoop and Hive
- Relevant certifications in cloud computing or data engineering
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.)