Senior Technical Consultant

Job description

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. Other Requirements Apply now Information at a Glance Why HCLTech? At HCLTech, you'll supercharge your potential. You'll find your career. And you'll find your spark. All at a place that knows that helping its customers stay on top starts by putting its people first. HCLTech is a global technology company, home to more than 230,000 people across 60 countries, delivering industry-leading capabilities centered around digital, engineering, cloud and AI, powered by a broad portfolio of technology services and products. We work with clients across all major verticals, providing industry solutions for Financial Services, Manufacturing, Life Sciences and Healthcare, Technology and Services, Telecom and Media, Retail and CPG, and Public Services. Consolidated revenues as of 12 months ending September 2026 totaled $14.8 billion. Disclaimer Privacy Statement Terms of use Sitemap Raise a Grievance We use cookies on our site. Please read more about cookies policy here. Opens in a new tab. Opens in a new tab. Opens in a new tab. Opens in a new tab. Opens in a new tab. Opens in a new tab. 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