Lead Engineer – GenAI, Agentic AI, and Knowledge Graph Architect

Job Description: We are seeking a Lead Engineer – GenAI, Agentic AI, and Knowledge Graph Architect 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.

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: Architect and implement neuro-symbolic AI solutions that combine:
  • Large Language Models and multimodal foundation models
  • Symbolic reasoning, business rules, constraints, and policy engines
  • Knowledge graphs and ontologies for grounding, reasoning, governance, and explainability
• Design and implement enterprise knowledge graph architectures using appropriate graph paradigms and technologies, including:
  • Property graphs and labeled property graph models
  • RDF, RDFS, OWL, SHACL, and semantic knowledge graphs
  • Graph databases and platforms such as Neo4j, Amazon Neptune, Stardog, GraphDB, TigerGraph, JanusGraph, ArangoDB, or equivalent technologies
• Design graph data models, schemas, ontologies, taxonomies, and canonical domain models aligned with enterprise use cases and data-governance requirements.
• Develop and optimize graph queries and traversal patterns using technologies such as:
  • Cypher, SPARQL, Gremlin, GraphQL, or vendor-specific graph query languages
  • Graph indexing, partitioning, caching, and performance-optimization strategies
• Lead the design and implementation of agentic AI systems, including:
  • Multi-agent orchestration
  • Tool use and function calling
  • Planning, reflection, routing, and task decomposition
  • Human-in-the-loop workflows
  • Agent memory and persistent state
  • Failure recovery, observability, evaluation, and governance
• Architect and deploy scalable APIs (REST/WebSocket) for AI and agent workflows.
• 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.).
• Provide technical leadership and mentorship to AI and platform engineers.
• Collaborate with cross‑functional teams to deliver AI solutions across banking, insurance, and healthcare domains.
• Ensure governance, compliance, observability, and robustness of AI systems.
• Stay current with advancements in Generative AI, agentic systems, symbolic reasoning, and knowledge‑centric AI.
• Document system designs and present solutions to both technical and non‑technical stakeholders.

Qualifications: Required Qualifications

• Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Data Science, Machine Learning , or related field.
• 7+ years of overall professional experience in AI/ML, Data Science, or advanced software engineering.
• 5+ years of strong hands‑on experience in Python , with solid software engineering best practices.
• 3+ years of experience building Generative AI or Agentic AI systems , including production deployments.
• Hands‑on experience with LLMs , prompt engineering, and model integration.
• Practical experience with Knowledge Graph design and implementation .
• Deep understanding of knowledge graphs, graph data modeling, graph algorithms, and semantic technologies.
• Proficiency in one or more graph query languages such as Cypher, SPARQL, or Gremlin.
• Experienced with Semantic Web technologies and standards , including RDF (Resource Description Framework), OWL (Web Ontology Language), SPARQL , ontology modeling, reasoning engines, and graph-based knowledge representation for enterprise AI applications.
• Proven experience deploying production‑grade AI systems with scalability and reliability considerations.
• Solid understanding of data pipelines, ETL, and data modeling .

Preferred Qualifications

• Experience with LangGraph, AutoGen, LangChain , or similar agent orchestration frameworks.
• Experience designing multi‑agent systems and long‑horizon reasoning…