machine learning engineer for enterprise data products

EPAM develops enterprise software products, open source solutions, and technology accelerators.

• Design and build data engineering frameworks, reusable components, and templates such as CI/CD pipelines to boost productivity across data engineering teams;
• Establish best practices for building AI-ready Data Products with enterprise-grade quality;
• Extend the data engineering framework to enable AI Agents to discover and consume Data Products at scale;
• Develop and integrate AI-assistant capabilities to accelerate Data Product development and operations;
• Set up robust, scalable pipelines leveraging version control and CI/CD frameworks;
• Collaborate with international, agile teams to deliver engineering excellence;
• Provide actionable technical guidance to team peers to elevate engineering standards;
• Drive adoption of Lakehouse architecture principles across Data Product initiatives.

• 3+ Years of experience in Data Engineering with a proven track record of building enterprise-quality Data Products, preferably in Lakehouse architecture;
• Proficiency in Python development;
• Proficiency in version control systems such as GitHub and CI/CD frameworks such as GitHub Actions;
• Strong knowledge of Databricks technologies including PySpark, Delta Lake, and Unity Catalog, or equivalent technology;
• Strong knowledge in Agentic AI and frameworks such as LangGraph, MCP, and A2A;
• Strong knowledge of industry-standard microservices technologies including Kubernetes and Docker;
• Strong English communication skills, both written and spoken;
• Ability to provide actionable technical guidance and implement engineering excellence practices among agile team peers.

• No conditions specified