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 Technical Lead to lead Context & Ontology, a core engineering team responsible for building the enterprise intelligence layer that powers AI agents across the platform. In this role, you will define the enterprise knowledge graph, design scalable ontology models, and build the context management framework that enables AI systems to understand business entities and relationships across multiple domains such as Finance, Legal, Procurement, and HR.
As the technical leader for the pod, you will drive architecture decisions, mentor a team of engineers, collaborate with cross-functional teams, and establish governance for enterprise knowledge models that serve as the foundation for intelligent AI applications.

Roles & Responsibilities:
- Lead the architecture, design, and implementation of the enterprise Knowledge Graph (KG) and ontology framework that powers AI-driven enterprise applications.
- Design and maintain scalable knowledge graph schemas, including entity models, relationships, metadata, and business attributes across enterprise domains such as Finance, Legal, Procurement, and Human Resources.
- Own the development and evolution of the ContextLoader and OntologyRegistry services to ensure consistent context management and knowledge retrieval across the platform.
- Provide technical leadership and mentorship to a team of Knowledge Graph Engineers, Ontology Modelers, and Search & Retrieval Engineers.
- Define and govern ontology standards, schema evolution processes, and best practices to ensure consistency and scalability across enterprise applications.
- Design and implement bitemporal versioning strategies for knowledge graph data to support historical tracking, auditing, and future state management.
- Collaborate closely with the Platform Data Engineering team to design robust data ingestion, transformation, and synchronization pipelines.
- Work with business stakeholders and domain experts to translate complex enterprise knowledge into structured semantic models.
- Drive architectural reviews, design discussions, and continuous improvements for graph-based data platforms.
- Ensure high performance, scalability, reliability, and maintainability of the enterprise knowledge platform.

Professional & Technical Skills:
- 12–15 years of experience in software engineering, with significant experience designing enterprise-scale Knowledge Graphs, semantic data models, or ontology-driven platforms.
- Strong expertise in graph data modeling, ontology design principles, and semantic technologies.
- Hands-on experience with graph databases such as Neo4j, Amazon Neptune, TigerGraph, or similar platforms.
- Strong understanding of semantic web standards such as RDF, OWL, SPARQL, or equivalent graph technologies.
- Experience designing enterprise ontologies and collaborating with business domain experts to model complex organizational data.
- Good understanding of vector search, semantic search, retrieval-augmented generation (RAG), and AI-powered information retrieval systems.
- Experience designing scalable data architectures and integrating structured, semi-structured, and unstructured enterprise data sources.
- Strong knowledge of distributed systems, API design, and cloud-native application development.
- Excellent leadership, mentoring, and stakeholder management skills.
- Strong analytical and problem-solving abilities with a passion for building scalable enterprise platforms.

Additional Information:
- Experience: 12–15 years
- Role: Technical Lead – Context & Ontology
- Reporting To: Engineering Manager – Platform Core
- Location: Bangalore (Flexible)
- Experience working with AI/ML platforms, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), or enterprise AI solutions will be an added advantage.
- Exposure to cloud platforms such as Azure, AWS, or Google Cloud is preferred.
- Candidates with experience building enterprise semantic platforms, metadata management solutions, or large-scale graph-based applications are highly preferred.

15 years full time education

About Accenture

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