4+ RDF Jobs in India

Ideal Candidate

Strong Ontologist Profile with deep semantic (RDF) ontology experience in complex enterprise environments

Mandatory (Experience 1) – Must have 8+ years of total experience, with a minimum of 5 years in semantic (RDF) ontology building/structuring for a complex organization.

Mandatory (Experience 2) – Must have a demonstrated ability to design comprehensive, intuitive semantic ontologies within a large, complex environment.

Mandatory (Experience 3) – Must be able to relate requirements and findings to business needs and outcomes.

Mandatory (Tech skill 1) – Must be familiar with W3C industry standards, including SPARQL and OWL.

Preferred (Tech skill 2) – Knowledge of ontology/TOMS tools such as PoolParty (Semantic Web), TopQuadrant EDG, or GraphDB (Ontotext)

Mandatory (Communication) – Must be fluent in English, with the ability to work across a complex environment with many stakeholders.

Mandatory (Education) – Must have a degree in Computer Science or Library Science, preferably with a minor / concentration / certificate in Information Management or Library Automation.

Mandatory (Note) – Must be available to work during US time zones for at least 4 hours per day.

Preferred (Tech skill 1) – Experience with graph databases, taxonomies, and NLP strategies for optimization.

Preferred (Tech skill 2) – Knowledge of ontology/TOMS tools such as PoolParty (Semantic Web), TopQuadrant EDG, or GraphDB (Ontotext)

Preferred (Tech skill 3) – Understanding of content development and search/retrieval tools.

Roles & Responsibilities

• Design and build ontologies (knowledge models) that organize Dell's business information in a structured way.
• Connect data from different systems so that all information is consistent and easy to understand.
• Create relationships between products, customers, services, and other business data to improve search and AI capabilities.
• Work closely with business teams to understand their requirements and convert them into structured knowledge models.
• Ensure data is organized according to industry standards such as RDF, OWL, and SPARQL.
• Improve enterprise search, analytics, and AI-driven applications by maintaining accurate knowledge structures.
• Collaborate with developers, data engineers, AI teams, and business stakeholders on knowledge management projects.
• Manage and update ontologies as new products, services, and business information are introduced.
• Support knowledge graph and metadata initiatives to improve data quality and consistency.
• Use graph databases and ontology management tools (where applicable) to maintain semantic data models.

Ideal Candidate

• Strong Ontologist Profile with deep semantic (RDF) ontology experience in complex enterprise environments
• Mandatory (Experience 1) – Must have 7+ years of total experience, with 5+ years in semantic (RDF) ontology building/structuring for a complex/large enterprises
• Mandatory (Experience 2) – Must have a strong understanding of content and information management, data structures, and semantic metadata extraction
• Mandatory (Tech skill 1) – Must have strong experience with graph databases, taxonomies, and NLP strategies for optimization
• Mandatory (Tech skill 2) – Must have strong working experience of ontology/TOMS tools such as PoolParty (Semantic Web), TopQuadrant EDG, or GraphDB (Ontotext)
• Mandatory (Tech skill 3) – Must be familiar with W3C industry standards, including SPARQL and OWL.
• Mandatory (Communication) – Must be fluent in English, with strong stakeholder management skills to operate across a complex, multi-team environment.many stakeholders.
• Mandatory (Note) – Must be available to work during US time zones for at least 4 hours per day
• Preferred (Education) – Must have a degree in Computer Science or Library Science, preferably with a minor / concentration / certificate in Information Management or Library Automation
• Preferred (Tech skill 3) – Understanding of content development and search/retrieval tools.

Pattern Agentix is seeking a skilled engineer to design and develop a comprehensive knowledge graph that spans multiple biological domains—including molecular biology, biochemistry, biophysics, immunology, virology, pharmacology, and computational biology. The goal is to integrate heterogeneous datasets (ontologies, public databases, scientific literature) into a scalable, semantically rich graph that supports hypothesis generation and interdisciplinary research.

Responsibilities:

• Architecture & Design:

- Develop a scalable knowledge graph architecture using graph databases

- Design data models to capture entities (e.g., genes, proteins, chemical compounds, immune markers) and their relationships.

• Data Integration & Curation:

- Integrate data from domain-specific ontologies (e.g., Gene Ontology, ChEBI) and public repositories (e.g., UniProt, PDB).

- Build API connectors and automation pipelines for data ingestion.

- Leverage NLP tools to extract relationships from scientific literature with validation by domain experts.

• Collaboration & Documentation:

- Work closely with interdisciplinary teams and domain experts to ensure semantic accuracy.

- Document data models, integration methods, and curation guidelines.

Requirements:

• Proven experience in knowledge graph development and graph databases

• Strong programming skills (Python, Java, or similar) and experience with API development.

• Familiarity with semantic web technologies (RDF, ) and ontology management tools  

• Solid background in bioinformatics, computational biology, or related disciplines.

• Demonstrated ability to work with heterogeneous data sources and interdisciplinary projects.

Contract

We are seeking part time or full time on contract and will compensate to local norms. 

The Knowledge Graph Architect is responsible for designing, developing, and implementing knowledge graph technologies to enhance organizational data understanding and decision-making capabilities. This role involves collaborating with data scientists, engineers, and business stakeholders to integrate complex data into accessible and insightful knowledge graphs.

Work you’ll do

1. Design and develop scalable and efficient knowledge graph architectures.

2. Implement knowledge graph integration with existing data systems and business processes.

3. Lead the ontology design, data modeling, and schema development for knowledge representation.

4. Collaborate with IT and business units to understand data needs and deliver comprehensive knowledge graph solutions.

5. Manage the lifecycle of knowledge graph data, including quality, consistency, and updates.

6. Provide expertise in semantic technologies and machine learning to enhance data interconnectivity and retrieval.

7. Develop and maintain documentation and specifications for system architectures and designs.

8. Stay updated with the latest industry trends in knowledge graph technologies and data management.

The Team

Innovation & Technology anticipates how technology will shape the future and begins building future capabilities and practices today. I&T drives the Ideation, Incubation and scale of hybrid businesses and tech enabled offerings at prioritized offering portfolio and industry interactions.

It drives cultural and capability transformation from solely services – based businesses to hybrid businesses. While others bet on the future, I&T builds it with you.

I&T encompasses many teams—dreamers, designers, builders—and partners with the business to bring a unique POV to deliver services and products for clients.

Qualifications and Experience

Required:

1. Bachelor’s or Master’s degree in Computer Science, Information Technology, or a related field.

2. 6-10 years of professional experience in data engineering with Proven experience in designing and implementing knowledge graph systems.

3. Strong understanding of semantic web technologies (RDF, SPARQL, GraphQL,OWL, etc.).

4. Experience with graph databases such as Neo4j, Amazon Neptune, or others.

5. Proficiency in programming languages relevant to data management (e.g., Python, Java, Javascript).

6. Excellent analytical and problem-solving abilities.

7. Strong communication and collaboration skills to work effectively across teams.

Preferred:

1. Experience with machine learning and natural language processing.

2. Experience with Industry 4.0 technologies and principles

3. Prior exposure to cloud platforms and services like AWS, Azure, or Google Cloud.

4. Experience with containerization technologies like Docker and Kubernetes