EXL—Data Scientist

Job description

Description: We are seeking for a Gen AI Engineer – Neuro-Symbolic AI & Agentic Systems to design, develop, and deploy enterprise-scale intelligent systems that fuse Generative AI, autonomous agents, symbolic reasoning, and Knowledge Graphs. The ideal candidate will bring strong expertise in Python, LLM-based systems, agentic frameworks, and knowledge-centric AI, with hands-on experience delivering production-grade GenAI or agentic solutions grounded using Knowledge Graphs (Neo4j). As part of EXL’s Digital AI R&D Innovation team, you will lead the architecture and implementation of agentic, reasoning-driven AI platforms, mentor engineers, shape technical strategy, and enable scalable AI solutions across multiple enterprise domains. Requirements: Bachelor's/Master's in Engineering 2-5 years Responsibilities: · Lead the design and implementation of agentic AI systems using frameworks such as LangGraph, AutoGen, LangChain, or similar. · Develop neuro-symbolic AI solutions that integrate: o Large Language Models (LLMs) o Symbolic reasoning, rules, and constraints o Knowledge Graphs for grounding and explainability · Design and implement Knowledge Graphs using Neo4j for: o LLM grounding and hallucination mitigation o Agent memory, planning, and reasoning o Explainable multi-hop inference · Develop and optimize Cypher queries, graph schemas, and indexing strategies in Neo4j. · Integrate semantic reasoning capabilities using RDF, OWL, SPARQL, ontologies, and rule-based inference engines to enable neuro-symbolic AI, multi-hop reasoning, explainability, and knowledge-driven agent behavior. · Implement Graph-RAG and hybrid retrieval pipelines combining vector databases and Knowledge Graphs. · Deploy and maintain multiple GenAI / Agentic AI solutions in production, ensuring reliability, scalability, and security. · Integrate SQL, No-SQL, vector, and graph databases (Postgres, MongoDB, Neo4j, ChromaDB, etc.).