Document Intelligence & NLP
Hands-on experience building document ingestion, parsing, OCR, chunking, metadata extraction, classification, entity extraction and semantic enrichment pipelines.
Apache Iceberg Vector Store & Search
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.
Vector & Retrieval Engineering
Experience with embedding generation, vector indexing, similarity search, ANN concepts, metadata filtering, hybrid search, reranking and retrieval-quality evaluation for enterprise-scale RAG.
Apache Ossie Semantic Layer
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.
Knowledge Graph & Graph Querying
Experience designing knowledge graphs and modelling relationships between documents, entities and business concepts, including
GQL/Cypher
or equivalent graph-query approaches.
NetworkX & Graph Analytics
Robust Python and
NetworkX
experience for graph construction, traversal, relationship analysis, centrality, community detection and other graph algorithms.
Hybrid Knowledge Retrieval / RAG
Experience combining
Iceberg vector search + semantic models + graph retrieval + structured metadata
to create context-rich retrieval for RAG and agentic AI solutions.

📌 Data & Ai (Melbourne)
🏢 DWS
📍 Melbourne