/n

- /n /n Document Intelligence & NLP

/n Hands-on experience building document ingestion, parsing, OCR, chunking, metadata extraction, classification, entity extraction and semantic enrichment pipelines. /n

- /n Apache Iceberg Vector Store & Search

/n Strong experience using Apache Iceberg as the governed storage layer for document chunks, metadata and vector embeddings, with vector similarity search, indexing, filtering and retrieval over Iceberg-backed data. Current implementations can store embeddings in Iceberg tables and perform similarity search through vector-capable query engines, with indexes used to accelerate retrieval. /n

- /n Vector & Retrieval Engineering

/n Experience with embedding generation, vector indexing, similarity search, ANN concepts, metadata filtering, hybrid search, reranking and retrieval-quality evaluation for enterprise-scale RAG. /n

- /n Apache Ossie Semantic Layer





/n Experience with Apache Ossie for vendor-neutral semantic models covering datasets, fields, relationships, metrics and AI context, providing consistent business meaning to analytics and AI applications. /n

- /n Knowledge Graph & Graph Querying

/n Experience designing knowledge graphs and modelling relationships between documents, entities and business concepts, including GQL/Cypher or equivalent graph-query approaches. /n

- /n NetworkX & Graph Analytics

/n Robust Python and NetworkX experience for graph construction, traversal, relationship analysis, centrality, community detection and other graph algorithms. /n

- /n Hybrid Knowledge Retrieval / RAG

/n Experience combining Iceberg vector search + semantic models + graph retrieval + structured metadata to create context-rich retrieval for RAG and agentic AI solutions. /n /n

📌 Data & AI (Melbourne)
🏢 DWS
📍 Melbourne