Graph Integration with AI

Skill Set: Graph Integration with AI

Experience: 4–8 years overall, with minimum 2+ years relevant hands-on experience in Graph-AI integration

Budget: 25 LPA Max

Work Location: Deloitte USI locations – Bengaluru, Hyderabad, Chennai, or Pune

Shift Timing: 11:00 AM to 8:00 PM

Interview Process

- 2 Interview Rounds
- No Client Round Required

Mandatory Skills
- Hands-on experience in Knowledge Graph design, data modeling, and development.
- Strong experience with graph databases such as Neo4j, Amazon Neptune, TigerGraph, Stardog, or GraphDB.
- Proficiency in graph query languages, including GraphQL, Cypher, SPARQL, and Gremlin.
- Strong programming experience in Python.
- Ability to design, build, and optimize scalable graph-based solutions and integrate them with enterprise applications.
- Experience working with cloud platforms is mandatory.

Good to Have
- Healthcare domain experience.

Role Overview Experienced Knowledge Graph Expert to design, build, and maintain scalable knowledge graph solutions that power search, recommendations, data integration, and AI/ML applications across the organization. Work at the intersection of data modeling, semantic technologies, and software engineering to transform disparate data sources into connected, queryable knowledge.

Key Responsibilities

- Design and implement ontologies, taxonomies, and schemas (RDF/OWL, property graphs) that accurately model business entities and relationships.
- Build and maintain knowledge graph pipelines for entity extraction, resolution, linking, and enrichment from structured and unstructured data.
- Develop and optimize graph queries using SPARQL, Cypher, Gremlin,



or similar query languages.
- Integrate knowledge graphs with downstream applications, including search, recommendation engines, and LLM-based systems (e.g., retrieval-augmented generation).
- Collaborate with data engineers, ML engineers, and domain experts to identify new data sources and use cases for graph-based solutions.
- Ensure data quality, consistency, and governance across the knowledge graph, including entity deduplication and conflict resolution.
- Evaluate and select appropriate graph databases and tools (e.g., Neo4j, Amazon Neptune, TigerGraph, Stardog, GraphDB).
- Monitor and improve graph performance, scalability, and reliability in production environments.
- Stay current with advances in semantic web technologies, graph ML, and knowledge representation.
- Document data models, pipelines, and best practices for internal stakeholders.

Required Skills & Tech Stack
- Strong hands-on experience building and maintaining knowledge graphs or semantic data models.
- Proficiency with graph databases (Neo4j, Amazon Neptune, TigerGraph, etc.) and query languages (Cypher, SPARQL, Gremlin).
- Strong understanding of ontology design, RDF/OWL, and taxonomy development.
- Experience with entity resolution, named entity recognition (NER), and relation extraction techniques.
- Solid programming skills in Python or Java, including experience with relevant graph libraries (e.g., RDFLib, NetworkX, Apache Jena).
- Familiarity with data pipeline tools and workflows (e.g., Apache Airflow, Spark).
- Solid analytical and problem-solving skills with attention to data quality and detail.

Pay: Up to ₹2,500,000.00 per year

Work Location: In person

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