Responsibilities

• Design and implement ML systems lifecycle from problem framing to deployment.
• Collaborate with business stakeholders to translate objectives into ML solutions.
• Develop and automate data and ML pipelines for production use.
• Orchestrate data preparation workflows using Kubeflow or Airflow.
• Deploy, monitor, optimize, and maintain machine learning models in production.
• Utilize Google Vertex AI pipelines to streamline MLOps processes.
• Develop infrastructure as code for reproducible cloud environments.
• Ensure the reliability, availability, and scalability of deployed models and systems.
• Troubleshoot and resolve issues in deployed ML systems.
• Build robust monitoring frameworks for real-time model performance evaluation.

Requirements

• 3+ years of experience with MLOps workflows and practices.
• Knowledge of Google Cloud Platform.
• Expertise in Google Vertex AI and Google Vertex AI pipelines.
• Strong understanding of MLOps practices.
• Proficiency in Python.
• Experience with Terraform.
• Experience with scripting languages like PowerShell.
• Knowledge of monitoring and maintaining ML models in production environments.
• Background in designing and orchestrating data pipelines using technologies such as Kubeflow or Airflow.
• Minimum English language level of B1+.

Nice to have

• Familiarity with Generative AI fundamentals and applications.
• Knowledge of Google Cloud Dataflow.
• Skills in Groovy, Jenkins, and Poetry.

We offer

• International projects with top brands.
• Work with global teams of highly skilled, diverse peers.
• Healthcare benefits.
• Employee financial programs.
• Paid time off and sick leave.
• Upskilling, reskilling and certification courses.
• Unlimited access to the LinkedIn Learning library and 22,000+ courses.
• Global career opportunities.
• Volunteer and community involvement opportunities.
• EPAM Employee Groups.
• Award-winning culture recognized by Glassdoor, Newsweek and LinkedIn.