Senior Technical Consultant
Confirmed on the employer's own hiring board on Nov 5, 2026. First seen by Alion on Nov 3, 2026.
Job Summary
- 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
Strong 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.
Key Responsibilities
- 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
Strong 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.
Skill Requirements
- 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
Strong 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.