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Knowledge Graph Jobs in New York (NOW HIRING)

3B Staffing LLC

Graph Data Engineer

Manhattan, NY · On-site

$126K - $151K/yr

The graph data engineer is responsible for developing and enhancing Jefferies knowledge graph to empower several applications in the firm. You will work in close collaboration with software engineers ...

Carbon Arc

Senior Applied Scientist

Manhattan, NY · On-site

$120 - $150/hr

Maintain and evolve Carbon Arc's knowledge graph by enriching ontologies, improving linking logic, and optimizing downstream reasoning * Establish rigorous standards for model evaluation, monitoring ...

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Knowledge Graph information

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$10

$33

$130

How much do knowledge graph jobs pay per hour?

As of Sep 6, 2026, the average hourly pay for knowledge graph in New York is $33.95, according to ZipRecruiter salary data. Most workers in this role earn between $17.36 and $28.41 per hour, depending on experience, location, and employer.

What is a knowledge graph?

A Knowledge Graph job typically involves designing, building, and maintaining structured representations of data that map relationships between entities. Professionals in this role work with technologies like RDF, SPARQL, ontologies, and graph databases to enhance data integration, retrieval, and reasoning. These jobs are common in AI, search, and data science fields, helping organizations improve knowledge discovery and decision-making.

What are some typical daily responsibilities of a knowledge graph engineer?

As a Knowledge Graph Engineer, your typical day involves designing and developing ontologies, integrating diverse data sources, and implementing graph-based data models to enhance information accessibility. You may work closely with data scientists, software developers, and business analysts to gather requirements and translate them into scalable knowledge graph solutions. Regular tasks include writing SPARQL queries, performing data mapping, maintaining documentation, and troubleshooting graph data issues. Collaboration and ongoing learning are integral as this field rapidly evolves with new tools and best practices.

What are the key skills and qualifications needed to thrive in the knowledge graph position, and why are they important?

To thrive as a Knowledge Graph Engineer, you need strong skills in semantic web technologies, ontology modeling, and data integration, typically supported by a background in computer science or data science. Familiarity with tools like RDF, SPARQL, OWL, and knowledge graph platforms (e.g., Neo4j, GraphDB) is common, and certifications in data engineering or semantic technologies are beneficial. Effective communication, problem-solving abilities, and cross-functional collaboration are valuable soft skills in this field. These competencies are crucial for designing, implementing, and maintaining knowledge graphs that enable advanced data discovery and insights for organizations.

What are the most commonly searched types of Knowledge Graph jobs in New York?

The most popular types of Knowledge Graph jobs in New York are:

What are popular job titles related to Knowledge Graph jobs in New York?

For Knowledge Graph jobs in New York, the most frequently searched job titles are:

What job categories do people searching Knowledge Graph jobs in New York look for?

The top searched job categories for Knowledge Graph jobs in New York are:

Knowledge Graph jobs near you
Infographic showing various Knowledge Graph job openings in New York as of August 2026, with employment types broken down into 1% As Needed, 82% Full Time, 14% Part Time, 1% Temporary, and 2% Contract. Highlights an 89% Physical, 3% Hybrid, and 8% Remote job distribution, with an average salary of $70,620 per year, or $34 per hour.

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

ExlService Holdings, Inc.

New York, NY • On-site

$100K - $160K/yr

Full-time

Re-posted 13 days ago

Key responsibilities

  • Design, develop, and deploy enterprise-scale intelligent systems that fuse Generative AI, autonomous agents, symbolic reasoning, and Knowledge Graphs.

  • Lead the architecture and implementation of agentic AI platforms, including multi-agent orchestration, tool use, planning, reflection, and human-in-the-loop workflows.

  • Deploy and maintain scalable AI solutions in production, ensuring reliability, scalability, and security.

ExlService Holdings rating

7.8

Company rating: 7.8 out of 10

Based on 8 frontline employees who took The Breakroom Quiz

138th of 500 rated business services

Job description


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.
Responsibilities
  • 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.

Qualifications
Required Qualifications
  • Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Machine Learning, or related field.
  • 5+ 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 $100,000 - $160,000 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.

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