Lead Engineer – GenAI, Agentic AI, and Graph Architect

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, Tiger Graph, Janus Graph, 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/Web Socket) 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 Lang Graph, Auto Gen, Lang Chain, 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.