Senior AI Engineer – Knowledge & Retrieval

Role Overview

We are looking for an experienced Senior AI Engineer to design and build enterprise-grade knowledge and retrieval solutions supporting Generative AI, RAG, and Agentic AI applications.

The role requires strong hands-on experience in document intelligence, vector search, semantic modelling, knowledge graphs, and hybrid retrieval, with a strong focus on Python and Apache Iceberg.

Key Responsibilities

• Design and develop document intelligence pipelines, including ingestion, OCR, parsing, chunking, metadata extraction, classification, entity extraction, and semantic enrichment.
• Build governed storage and retrieval solutions using Apache Iceberg for document chunks, metadata, and vector embeddings.
• Implement embedding generation, vector indexing, similarity/ANN search, metadata filtering, hybrid search, and reranking for enterprise RAG.
• Develop semantic models using Apache Ossie, covering datasets, fields, relationships, metrics, and AI context.
• Design Knowledge Graphs connecting documents, entities, and business concepts, using GQL/Cypher or equivalent graph-query technologies.
• Use Python and NetworkX for graph construction, traversal, relationship analysis, centrality, community detection, and graph algorithms.
• Develop hybrid knowledge retrieval combining Iceberg vector search, semantic models, knowledge graphs, and structured metadata.
• Integrate retrieval capabilities with RAG and Agentic AI solutions, focusing on relevance, accuracy, and retrieval quality.

Required Skills

• Strong hands-on development experience with Python.
• Experience with Document Intelligence, NLP, OCR, document parsing and information extraction.
• Strong knowledge of Apache Iceberg, including storage of document data, metadata, and embeddings.
• Solid understanding of embeddings, vector databases/search, ANN, hybrid search, reranking, and retrieval evaluation.
• Experience building production-grade RAG and Generative AI solutions.
• Experience with Apache Ossie or similar semantic-layer technologies.
• Strong understanding of Knowledge Graphs and GQL/Cypher or equivalent graph-query approaches.
• Hands-on experience with NetworkX and graph analytics.
• Experience integrating vector, semantic, graph, and structured retrieval approaches.
• Experience working with GCP environments.

Preferred

• Experience building enterprise-scale GenAI / Agentic AI platforms.
• Understanding of data governance, metadata management, and enterprise security.
• Experience working with large-scale structured and unstructured enterprise data.

WHY YOU SHOULD JOIN US

• Hybrid working mode
• Attractive Package including full salary + 13th month salary
• 18 paid leaves/year (12 annual leaves and 6 personal leaves)
• Insurance plan based on full salary
• 100% full salary and benefits as an official employee from the 1st day of working
• Medical benefit for employees and family
• Working in a fast-paced, flexible, and multinational working environment. Chance to travel onsite (in 49 countries)
• Free snacks, refreshments, and parking
• Internal training (Technical & Functional & English)
• Hybrid working mode, flexible time