Data Scientist

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.

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.).

Qualifications

Bachelor's/Master's in Engineering 2-5 years