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

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. 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. 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 workflows. Knowledge of neuro‑symbolic AI concepts , logic‑based reasoning, or rule‑based systems. Experience with Graph Data Science (GDS) or graph‑based inference techniques. Familiarity with MLOps practices (CI/CD, monitoring, experimentation, retraining). Experience working in regulated or enterprise environments. Contributions to open‑source projects, internal AI platforms, or applied AI research . Strong problem‑solving skills and ability to thrive in fast‑paced R&D environments. The typical base pay range for this role across the U.S. is USD $140,000 - $200,000 per year. For more information on benefits and what we offer please visit us at https://www.exlservice.com/us-careers-and-benefits The posted range is the hiring range for this role — a subset of the broader range available to employees over time — and reflects base salary across our national hiring scale. Final offers are based on several factors, including the candidate's skills and experience, internal pay equity, work location, market conditions for the role, and the specific scope and responsibilities of the position. The top of the range is reserved for candidates who notably exceed the requirements; the lower end applies to those with less experience or fewer preferred qualifications. For positions based in higher-cost zones (e.g., California, New York, New Jersey), actual compensation may exceed the posted range; your recruiter will share specifics during the process.