Lead Architect

• 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

• 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 .

Core Competencies

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 Implementation
• Generative AI or Agentic AI Systems
• Graph Query Languages (Cypher, SPARQL, Gremlin)
• Python Programming
• Semantic Web Technologies (RDF, OWL)

ATS Optimization Keywords

Hard Skills

• Knowledge Graph Design
• Graph Data Modeling
• Graph Algorithms
• Generative AI Systems
• Agentic AI Systems
• Python Programming
• Data Pipelines
• ETL
• Graph Query Languages
• Software Engineering Best Practices

Soft Skills

• Technical Leadership
• Mentorship
• Collaboration
• Communication

Industry Keywords

• Artificial Intelligence
• Machine Learning
• Data Science
• Banking
• Insurance
• Healthcare

Tools & Technologies

• Neo4j
• Amazon Neptune
• Stardog
• GraphDB
• TigerGraph
• JanusGraph
• ArangoDB
• REST APIs
• WebSocket APIs
• Graph Databases