Lead Engineer – GenAI, Agentic AI, Knowledge Graph Architect

EXL

Lead Engineer for designing and deploying enterprise-scale intelligent systems that integrate Generative AI and Knowledge Graphs at EXL. Lead implementation and mentorship within the Digital AI R&D Innovation team.

Posted 7/24/2026full-timeNew York City • New York • 🇺🇸 United StatesSenior💰 $140,000 - $200,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in architecting and implementing neuro-symbolic AI solutions, with a strong focus on knowledge graph design, generative AI systems, and scalable API deployment. Proficient in leading cross-functional teams to deliver robust AI solutions while ensuring compliance and governance across various domains.

Highest-signal resume keywords

Knowledge Graph Design and ImplementationGenerative AI or Agentic AI SystemsGraph Query Languages (Cypher, SPARQL, Gremlin)Python ProgrammingSemantic Web Technologies (RDF, OWL)

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Knowledge Graph DesignGraph Data ModelingGraph AlgorithmsGenerative AI SystemsAgentic AI SystemsPython ProgrammingData PipelinesETLGraph Query LanguagesSoftware Engineering Best Practices

Soft Skills

Technical LeadershipMentorshipCollaborationCommunication

Tools & Technologies

Neo4jAmazon NeptuneStardogGraphDBTigerGraphJanusGraphArangoDBREST APIsWebSocket APIsGraph Databases

Industry Keywords

Artificial IntelligenceMachine LearningData ScienceBankingInsuranceHealthcare

Tech Stack

Tools & technologies

ETLGraphQLMongoDBNeo4jPostgresPythonSQL

About the role

Key responsibilities & impact

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.

Requirements

What you’ll need

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**.

Benefits

Comp & perks

For more information on benefits and what we offer please visit us at https://www.exlservice.com/us-careers-and-benefits