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описание
EPAM develops enterprise software products, open source solutions, and accelerators.
задачи
Embed fully within the PLM team to understand supplier specification workflows, test method structures, and properties management;
Design and implement document ingestion pipelines for PDF, Excel, and Word supplier specification files using Databricks ai_parse_document;
Build and maintain Agent Bricks Information Extraction pipelines against the canonical PLM data model;
Develop and optimize LLM-powered grouping and recommendation logic using ai_query and Databricks Model Serving;
Create and maintain the Databricks Apps front end with React and Python, including file upload, metadata grids, forms, review and approval screens, and write-back to Lakebase;
Integrate the Graph Database layer for relationships between specifications, properties, test methods, and AI recommendations;
Configure and maintain Azure Private Link and Private Endpoint connectivity, including DNS forwarding for databricksapps.com;
Implement structured logging, Unity Catalog lineage tracking, and audit trails for Responsible AI governance;
Contribute to ARB artifacts and technical design documentation;
Collaborate with the Business Analyst to translate PLM requirements into data models and pipeline logic;
Collaborate with the Delivery Manager to surface risks and effort estimates.
требования
5+ Years of experience in AI or data engineering roles;
At least 1 year of relevant leadership experience;
Expertise in Databricks, including Unity Catalog, Volumes, and Model Serving;
Proficiency in ai_parse_document and Agent Bricks;
Strong Python skills, including PySpark, pandas, and FastAPI;
Experience with React front-end development, hooks, and REST integration;
Competency in Azure, including Private Link, Private Endpoints, and VNet;
Knowledge of LLM prompt engineering and ai_query;
Familiarity with Graph Databases, such as Neo4j or equivalent;
Understanding of Delta Lake and Lakebase;
Ability to design and integrate REST APIs;
Experience with Azure DevOps;
English proficiency at Upper-Intermediate level (B2) or higher;
Nice to have: Experience with GxP or regulated manufacturing data environments, exposure to PLM or ERP systems such as SAP, Teamcenter, or Enovia, Databricks certification, background in graph data modeling and lineage use cases.