Large Language Model Architect
BengaluruFull-timePosted Jul 29, 2026
Project Role : Large Language Model Architect
Project Role Description : Architect large language models (LLM) that can process and generate natural language. Design neural network parameters, trained on large quantities of unlabeled text data.
Must have skills : Large Language Models (LLMs), Generative AI, Virtual Agents
Good to have skills : NA
Minimum 15 year(s) of experience is required
Educational Qualification : 15 years full time education
Summary:
We are looking for an experienced Ontology Modeler to join Context & Ontology, the team responsible for building the enterprise knowledge foundation that powers AI agents across the platform. In this role, you will work closely with business leaders and subject matter experts across Finance, Legal, Procurement, HR, and other enterprise functions to capture business knowledge and translate it into structured, reusable ontology models.
As an Ontology Modeler, you will bridge the gap between business and technology by creating standardized semantic models that enable AI systems to consistently understand enterprise concepts, relationships, and business rules. You will play a key role in defining enterprise-wide knowledge models, resolving semantic inconsistencies across business domains, and ensuring that the organization's knowledge assets are accurate, scalable, and aligned with business objectives.
Roles & Responsibilities:
- Collaborate with business stakeholders and domain experts across Finance, Legal, Procurement, HR, and other enterprise functions to understand business processes, terminology, and knowledge structures.
- Lead knowledge discovery and domain modeling workshops to identify, validate, and formalize business concepts into enterprise ontology models.
- Translate complex business knowledge into structured ontology schemas, including entities, relationships, attributes, taxonomies, and business rules.
- Develop, maintain, and enhance enterprise ontology documentation to ensure concepts are well-defined, discoverable, and easily understood by both technical and business teams.
- Own the lifecycle management of ontology concepts, including creation, modification, governance, versioning, and retirement of business concepts.
- Identify semantic inconsistencies and conflicting business definitions across departments, and facilitate alignment to establish a unified enterprise vocabulary.
- Partner with Knowledge Graph Engineers and Data Product Engineers to map enterprise data assets to ontology concepts and ensure semantic consistency across data platforms.
- Support the design and continuous improvement of enterprise knowledge graphs by validating ontology models against evolving business requirements.
- Work closely with engineering teams to ensure ontology models can be effectively implemented within graph databases, semantic platforms, and AI-powered applications.
- Participate in governance reviews, ontology quality assessments, and enterprise data standardization initiatives.
- Stay current with emerging trends in ontology engineering, semantic technologies, enterprise metadata management, and knowledge representation.
Professional & Technical Skills:
- 12–15 years of experience in knowledge management, ontology modeling, semantic technologies, enterprise architecture, data modeling, or related domains.
- Hands-on experience in ontology development, knowledge elicitation, semantic modeling, or enterprise information modeling within large organizations.
- Strong understanding of ontology design principles, taxonomy development, semantic data modeling, and knowledge representation techniques.
- Familiarity with Knowledge Graph concepts, graph databases, RDF, OWL, SPARQL, or similar semantic web technologies is highly desirable.
- Experience working directly with senior business stakeholders and subject matter experts to capture, validate, and structure complex business knowledge.
- Proven ability to resolve cross-functional semantic conflicts and establish standardized enterprise terminology across multiple business domains.
- Strong understanding of enterprise business processes, particularly within Finance, Procurement, Legal, Human Resources, or related corporate functions.
- Experience documenting ontology models, business glossaries, metadata standards, and semantic governance frameworks.
- Excellent analytical thinking, communication, facilitation, and stakeholder management skills.
- Ability to bridge business and technical teams by translating business requirements into structured semantic models that support enterprise AI and analytics initiatives.
Additional Information:
- Experience 12–15 years
- Role Ontology Modeler – Context & Ontology
- Reporting To Senior Knowledge Graph / Ontology Engineer (Tech Lead)
- Location Bangalore (Flexible)
- Experience with Knowledge Graph platforms, enterprise metadata management, semantic search, AI/ML, Large Language Models (LLMs), or Retrieval-Augmented Generation (RAG) solutions will be an added advantage.
- Familiarity with enterprise architecture frameworks, data governance, and master data management concepts is preferred.
- Candidates with experience building enterprise ontologies, business glossaries, semantic data models, or knowledge management platforms will be highly preferred.15 years full time education
Project Role Description : Architect large language models (LLM) that can process and generate natural language. Design neural network parameters, trained on large quantities of unlabeled text data.
Must have skills : Large Language Models (LLMs), Generative AI, Virtual Agents
Good to have skills : NA
Minimum 15 year(s) of experience is required
Educational Qualification : 15 years full time education
Summary:
We are looking for an experienced Ontology Modeler to join Context & Ontology, the team responsible for building the enterprise knowledge foundation that powers AI agents across the platform. In this role, you will work closely with business leaders and subject matter experts across Finance, Legal, Procurement, HR, and other enterprise functions to capture business knowledge and translate it into structured, reusable ontology models.
As an Ontology Modeler, you will bridge the gap between business and technology by creating standardized semantic models that enable AI systems to consistently understand enterprise concepts, relationships, and business rules. You will play a key role in defining enterprise-wide knowledge models, resolving semantic inconsistencies across business domains, and ensuring that the organization's knowledge assets are accurate, scalable, and aligned with business objectives.
Roles & Responsibilities:
- Collaborate with business stakeholders and domain experts across Finance, Legal, Procurement, HR, and other enterprise functions to understand business processes, terminology, and knowledge structures.
- Lead knowledge discovery and domain modeling workshops to identify, validate, and formalize business concepts into enterprise ontology models.
- Translate complex business knowledge into structured ontology schemas, including entities, relationships, attributes, taxonomies, and business rules.
- Develop, maintain, and enhance enterprise ontology documentation to ensure concepts are well-defined, discoverable, and easily understood by both technical and business teams.
- Own the lifecycle management of ontology concepts, including creation, modification, governance, versioning, and retirement of business concepts.
- Identify semantic inconsistencies and conflicting business definitions across departments, and facilitate alignment to establish a unified enterprise vocabulary.
- Partner with Knowledge Graph Engineers and Data Product Engineers to map enterprise data assets to ontology concepts and ensure semantic consistency across data platforms.
- Support the design and continuous improvement of enterprise knowledge graphs by validating ontology models against evolving business requirements.
- Work closely with engineering teams to ensure ontology models can be effectively implemented within graph databases, semantic platforms, and AI-powered applications.
- Participate in governance reviews, ontology quality assessments, and enterprise data standardization initiatives.
- Stay current with emerging trends in ontology engineering, semantic technologies, enterprise metadata management, and knowledge representation.
Professional & Technical Skills:
- 12–15 years of experience in knowledge management, ontology modeling, semantic technologies, enterprise architecture, data modeling, or related domains.
- Hands-on experience in ontology development, knowledge elicitation, semantic modeling, or enterprise information modeling within large organizations.
- Strong understanding of ontology design principles, taxonomy development, semantic data modeling, and knowledge representation techniques.
- Familiarity with Knowledge Graph concepts, graph databases, RDF, OWL, SPARQL, or similar semantic web technologies is highly desirable.
- Experience working directly with senior business stakeholders and subject matter experts to capture, validate, and structure complex business knowledge.
- Proven ability to resolve cross-functional semantic conflicts and establish standardized enterprise terminology across multiple business domains.
- Strong understanding of enterprise business processes, particularly within Finance, Procurement, Legal, Human Resources, or related corporate functions.
- Experience documenting ontology models, business glossaries, metadata standards, and semantic governance frameworks.
- Excellent analytical thinking, communication, facilitation, and stakeholder management skills.
- Ability to bridge business and technical teams by translating business requirements into structured semantic models that support enterprise AI and analytics initiatives.
Additional Information:
- Experience 12–15 years
- Role Ontology Modeler – Context & Ontology
- Reporting To Senior Knowledge Graph / Ontology Engineer (Tech Lead)
- Location Bangalore (Flexible)
- Experience with Knowledge Graph platforms, enterprise metadata management, semantic search, AI/ML, Large Language Models (LLMs), or Retrieval-Augmented Generation (RAG) solutions will be an added advantage.
- Familiarity with enterprise architecture frameworks, data governance, and master data management concepts is preferred.
- Candidates with experience building enterprise ontologies, business glossaries, semantic data models, or knowledge management platforms will be highly preferred.15 years full time education
About Accenture
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