Job Description
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- /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 Solid 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 Strong 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