Federated Intelligence Engineer - AIML

We are looking for a Federated Intelligence Engineer to build intelligent systems that bring together distributed knowledge, semantic reasoning, hybrid retrieval, and LLM-based agents. The role focuses on creating solutions where Knowledge Graphs, GraphRAG, vector retrieval, ontologies, reasoning engines, and Agentic AI work together to provide accurate, explainable, and efficient answers. Key Responsibilities Design and implement GraphRAG and hybrid retrieval solutions combining vector search with graph-based retrieval and expansion. Build and maintain knowledge graphs and ontologies using RDF, OWL, and SPARQL. Model complex domain relationships, including causal and cause-effect relationships. Implement semantic reasoning using technologies such as RDFox and/or SemReasoner. Combine knowledge-graph retrieval with embeddings and vector databases such as ChromaDB. Develop GenAI backend components and services using TypeScript and Node.js. Build LLM agent tools and MCP servers that enable models to interact reliably with enterprise knowledge and services. Design effective tool descriptions, interfaces, and context that guide LLM tool selection and behavior. Benchmark and continuously improve answer quality, retrieval accuracy, reasoning effectiveness, and token consumption. Troubleshoot issues across the complete retrieval and reasoning pipeline and address root causes rather than symptoms. Support containerized and multi-service development environments. Must-Have Skills Hands-on experience with GraphRAG and/or hybrid retrieval Vector search combined with Knowledge Graph traversal/expansion Strong understanding of Knowledge Graphs and semantic technologies Strong knowledge of RDF, OWL, and SPARQL Experience with RDFox, SemReasoner, or similar reasoning-capable triple stores Hands-on experience in ontology modelling Experience modelling causal/cause-effect relationships Practical experience with embeddings and vector databases, preferably ChromaDB Strong TypeScript and Node.js development skills Experience developing LLM agent tools Experience with or strong exposure to Model Context Protocol (MCP) servers Strong engineering mindset around evaluation, benchmarking, answer quality, and token-cost optimization Strong problem-solving mindset with emphasis on root-cause analysis Nice-to-Have Skills Reasoning-based validation Time-series and signal analysis Cross-correlation and anomaly detection Docker Shell scripting Multi-service/containerized development environments