Knowledge Graph and Ontology Engineer

Vadodara - India

Monthly Salary: Not Disclosed

Posted on: 3 hours ago

Vacancies: 1 Vacancy

Job Summary

Key Responsibilities
Design and evolve EPC-domain ontologies (e.g. equipment tags P&IDs
materials vendor docs schedules assets) using industry best practices and
governance standards.
Develop and maintain enterprise knowledge graphs using RDF/OWL and/or
property graph paradigms to unify structured and unstructured data across Fluor
business lines.
Create semantic data models taxonomies and controlled vocabularies to
improve metadata quality discoverability and interoperability across systems.
Implement ingestion and semantic mapping pipelines (ETL/ELT) from
engineering and enterprise sources (e.g. document repositories BIM EAM/ERP
procurement project controls) into graph stores.
Define entity resolution and relationship strategies (matching deduplication
golden records) for critical EPC entities such as equipment lines instruments
materials vendors and assets.
Enable semantic search and retrieval (SPARQL/Gremlin/Cypher vector
graph hybrid patterns) to support AI copilots and knowledge-driven applications.

Establish standards for data governance and stewardship including naming
conventions versioning lineage and ontology change management.
Partner with engineering SMEs to validate models against real project use-
cases (FEED EPC execution commissioning/turnover operations) and ensure
fitness for purpose.
Optimize graph performance and scalability (indexing query tuning
partitioning caching incremental updates) for enterprise-grade workloads.
Develop documentation and enablement (model specs data dictionaries
example queries best-practice guides) to accelerate adoption by teams globally.
Support integration with analytics/AI platforms (feature generation graph
embeddings RAG pipelines MLOps interfaces) while ensuring responsible data
usage.
Contribute to roadmap planning for semantic capabilities tooling selection
and reusable accelerators across Fluors EPC portfolio.

Required Qualifications
Bachelors degree in Computer Science Data Engineering Information
Systems or a related field (or equivalent practical experience).
3 years building knowledge graphs ontologies or semantic data solutions in
production environments.
Hands-on experience with RDF/OWL SPARQL and ontology engineering
practices (e.g. SHACL reasoning versioning).
Experience with graph databases and query languages (e.g. Neo4j/Cypher
Amazon Neptune/Gremlin RDF triplestores).
Strong programming skills in Python (and/or Java/Scala) with proven data
pipeline development experience.
Understanding of data modeling (conceptual/logical/physical) metadata
management and data quality.
Ability to translate business/domain concepts into formal semantic
representations and implement them effectively.
Strong communication skills-able to collaborate with both technical teams and
EPC domain stakeholders.

Preferred Qualifications
Experience in EPC engineering construction asset-intensive industries
(energy chemicals mining & metals infrastructure data centers etc.).
Familiarity with industry standards such as ISO 15926 CFiHOS IEC/ISA naming
conventions or engineering tag conventions.
Experience integrating data from systems commonly used in EPC environments
(e.g. AVEVA/SmartPlant Bentley SAP Maximo Primavera P6 Aconex
EDMS).
Knowledge of entity resolution/MDM knowledge graph vector search
patterns and RAG architectures.
Exposure to Azure or AWS data/AI services and CI/CD for data products (e.g.
Git DevOps pipelines).
Prior work with governance frameworks and stewardship operating models for
enterprise data products.

Education & Certifications
Education: Bachelors degree required; Masters in CS/Data
Science/AI/Information Management preferred.
Certifications: Cloud (Azure/AWS) Data Management (e.g. DAMA/metadata)
graph/semantic web certifications if available.

Experience
Typically 3 8 years relevant experience (flexible based on demonstrated
impact).