ai engineer for PLM solutions

The team develops an AI-powered Product Lifecycle Management solution that processes supplier specifications and generates structured recommendations for human review. The solution uses Databricks, Azure, React, Python, and graph technologies for approximately 30 business users.

• Embed fully within the PLM team to understand supplier specification workflows, test method structures, and properties management;
• Design and implement a document ingestion pipeline using Databricks ai_parse_document for PDF, Excel, and Word supplier specification files, storing artifacts in Unity Catalog Volumes;
• 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, human-in-the-loop 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, lineage tracking in Unity Catalog, 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.

• 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;
• Python skills with PySpark, pandas, and FastAPI;
• React front-end development experience with hooks and REST integration;
• Azure expertise with 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 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.

• Remote work in Georgia.