An Agentic AI Engineer is required to design and deliver production-grade AI solutions capable of extracting knowledge from unstructured content, connecting multiple enterprise data sources, and enabling AI agents to retrieve and act on company data.

Responsibilities:

• Build an entity extraction pipeline using GLiNER and LLM-based relationship extraction.
• Design, deploy and optimise Qdrant based vector database for enterprise data retrieval.
• Build and maintain Neo4j knowledge graphs, integrating and resolving entities from diverse data sources.
• Develop LangGraph agent workflows to route user queries and retrieve information from multiple data sources.
• Build external API integrations for third-party data sources.
• Transform an existing AI prototype into a scalable, production-ready, reusable agentic service.
• Deliver robust AI solution that integrate knowledge graphs, vector search, document processing and agent orchestration.

Required Experience

• Experience building Agentic AI solutions using LangGraph or a similar orchestration framework.
• Strong Python development experience, including delivering production-grade AI services.
• Experience in combining lightweight zero-shot encoders (GLiNER) with LLMs for structured data extraction
• Hands-on experience deploying and managing vector databases.
• Experience designing and implementing Neo4j knowledge graphs and entity resolution.
• Strong understanding of embeddings, retrieval pipelines, vector search and AI agent architectures.
• Experience integrating structured and unstructured enterprise data sources.