Lead AI/ML Data Architect at DataVail

Led data and AI architecture initiatives involving semantic data modeling, enterprise data integration, knowledge representation, and AI-enabled information retrieval. Designed conceptual and logical models defining entities, attributes, relationships, business terminology, metadata, and dependencies across complex data domains. Applied knowledge graph, ontology, taxonomy, and semantic modeling concepts to improve data discovery, integration, search, and AI application development. Built Python/SQL/Spark/DataBricks pipelines integrating structured and semi-structured data and preparing reliable datasets for downstream applications. Collaborated with data engineers, architects, analysts, and subject matter experts to translate business requirements into practical schemas, relationships, mappings, and semantic structures. Supported graph-oriented and RAG solutions by organizing entities, metadata, relationships, and contextual information for improved retrieval and AI reasoning. Document

Lead AI/ML Engineer / Data Architect at N/A

Led data and AI architecture initiatives involving semantic data modeling, enterprise data integration, knowledge representation, and AI-enabled information retrieval. Designed conceptual and logical models defining entities, attributes, relationships, business terminology, metadata, and dependencies across complex data domains. Applied knowledge graph, ontology, taxonomy, and semantic modeling concepts to improve data discovery, integration, search, and AI application development. Built Python/SQL/PySpark/DataBricks pipelines to integrate structured and semi-structured data and prepare reliable datasets for downstream applications. Collaborated with data engineers, architects, analysts, and domain experts to translate business requirements into practical schemas, relationships, mappings, and semantic structures. Supported graph-oriented and RAG solutions by organizing entities, metadata, relationships, and contextual information for improved retrieval and AI reasoning. Documented data

Lead AI/ML & Data Architect at Lead AI M L / & Data

Led data and AI architecture initiatives spanning semantic data modeling, enterprise data integration, knowledge representation, and AI-enabled information retrieval. Designed conceptual and logical models defining entities, attributes, relationships, business terminology, metadata, and dependencies across complex data domains. Applied knowledge graph, ontology, taxonomy, and semantic modeling concepts to improve data discovery, integration, search, and AI application development. Built Python, SQL, PySpark, and DataBricks pipelines to integrate structured/semi-structured data and prepare reliable datasets for downstream use. Collaborated with data engineers, architects, analysts, and subject-matter experts to translate business requirements into practical schemas, relationship mappings, and semantic structures. Supported graph-oriented and RAG solutions by organizing entities, metadata, and contextual information for improved retrieval and AI reasoning. Documented data models, semanti

Sr AI/ML Data Engineer at Deaconess Health Systems

Designed healthcare data models supporting clinical, operational, population-health analytics, and digital-health initiatives. Mapped healthcare entities, clinical concepts, attributes, relationships, and business terminology across multiple enterprise data sources. Developed Python, SQL, Spark, and DataBricks pipelines to integrate, standardize, validate, and transform healthcare data. Supported semantic and canonical data structures to improve consistency and interoperability across healthcare applications and analytics platforms. Worked with clinical SMEs, analysts, engineers, and business stakeholders to translate complex requirements into maintainable data and semantic models. Applied metadata, taxonomy, data-quality, and governance practices to improve consistency and usability of healthcare information. Performed root-cause analysis on data integration and modeling issues, documenting mappings, definitions, relationships, and transformation rules.

AI/ML Engineer at DataBricks

Developed scalable data and AI solutions using Python, SQL, Apache Spark, DataBricks, and Delta Lake for enterprise data and analytics workloads. Designed logical data structures representing entities, attributes, relationships, metadata, and dependencies across diverse datasets. Built ingestion and transformation pipelines to normalize and organize structured and semi-structured data for analytics and machine learning. Supported data discovery, lineage, governance, schema management, and metadata practices within modern lakehouse environments. Worked with data engineers, data scientists, and architects to improve data organization and accessibility for analytical and AI use cases. Applied data modeling and relationship analysis techniques to support semantic search, knowledge-oriented applications, and downstream analytics. Created technical documentation covering schemas, mappings, transformation logic, data relationships, and integration dependencies.