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.
Required Qualifications
Preferred Qualifications
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.
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.
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.