Sr. Engineer, Knowledge Engineering

This position is posted by Jobgether on behalf of a partner company. We are currently looking for a Sr. Engineer, Knowledge Engineering in India.

This role sits at the intersection of data modeling, semantic systems, and AI enablement, focusing on transforming complex enterprise data into structured knowledge representations that machines can reason over. You will design and build ontologies and knowledge graphs that connect core business entities such as suppliers, contracts, invoices, and transactions into a unified semantic layer. This work directly powers advanced AI capabilities, enabling reasoning, inference, and smarter automation across large-scale spend management systems. You will collaborate closely with ML engineers and platform teams to ensure knowledge structures are usable, scalable, and AI-ready. The environment is highly technical and innovation-driven, with a strong emphasis on building foundational systems that support next-generation AI features. This is a high-impact engineering role where your work becomes a core part of the intelligence layer powering enterprise decision-making.

• Design and implement ontologies based on complex enterprise data models, defining entities, relationships, and business rules across spend management domains.
• Build and maintain scalable knowledge graphs that represent structured relationships between business entities such as suppliers, contracts, invoices, and purchase orders.
• Develop graph query interfaces and APIs to enable AI systems and applications to consume and reason over knowledge graph data.
• Design and evolve multi-tenant and shared knowledge graph architectures, ensuring proper isolation, scalability, and performance.
• Map existing taxonomies, classifications, and hierarchical structures into ontology-driven graph representations.
• Collaborate with ML and AI engineering teams to generate ontology-aware datasets for model training and evaluation.
• Evaluate and select appropriate graph storage and query technologies (e.g., Neo4j, Neptune, Elasticsearch graph capabilities) based on scalability and performance needs.
• Continuously evolve and refine the ontology framework as new business domains and AI capabilities are introduced.

Requirements:

• 5+ years of software engineering experience, including at least 2+ years focused on knowledge graphs, data modeling, or semantic systems.
• Strong experience with graph databases or graph query technologies such as Neo4j, Amazon Neptune, or Elasticsearch graph features.
• Solid understanding of ontology design principles, including RDF, JSON-LD, OWL, or similar semantic modeling approaches.
• Experience building entity resolution systems and populating knowledge graphs from structured enterprise data sources.
• Strong data modeling skills, including entity-relationship design, hierarchical taxonomies, and schema design.
• Proficiency in Python and graph query languages such as Cypher, Gremlin, SPARQL, or Elasticsearch DSL.
• Experience working with enterprise-scale B2B SaaS data models is a strong plus.
• Bachelor’s or Master’s degree in Computer Science or equivalent practical experience.
• Strong analytical thinking, collaboration skills, and ability to work in complex, data-rich environments.

Benefits:

• Opportunity to work on cutting-edge AI and knowledge graph technologies powering enterprise-scale platforms.
• Exposure to large-scale, real-world business data across global spend management ecosystems.
• Collaborative and innovation-driven engineering culture focused on building foundational AI systems.
• Strong emphasis on transparency, inclusion, and cross-functional teamwork.
• Opportunity to influence core data architecture and AI reasoning capabilities at scale.
• Competitive compensation and career growth opportunities in a high-impact technical role.
• Inclusive and equal-opportunity work environment with strong support for professional development.