Lead Systems Engineer - Data DevOps/MLOps

As a Lead Systems Engineer with Data DevOps/MLOps expertise at EPAM, your role will involve driving innovation and efficiency across data and machine learning operations. Here are the key responsibilities you will be handling: - 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 Qualifications required for this role include: - Bachelors or Masters 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 skills: - 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 Additionally, EPAM offers a comprehensive benefits package that includes health benefits, retirement benefits, paid time off, flexible benefits, and forums to explore beyond work passion such as CSR, photography, painting, sports, etc. You will also have the opportunity for self-development through online university, knowledge sharing opportunities globally, learning opportunities through external certifications, and sponsored Tech Talks & Hackathons. EPAM also provides unlimited access to LinkedIn learning solutions and the possibility to relocate to any EPAM office for short and long-term projects. As a Lead Systems Engineer with Data DevOps/MLOps expertise at EPAM, your role will involve driving innovation and efficiency across data and machine learning operations. Here are the key responsibilities you will be handling: - 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 Qualifications required for this role include: - Bachelors or Masters 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