Principal AI Architect

Job Description: AI Architect Knowledge Engineering, Ontologies, OWL 2, SHACL

Location: Hyderabad, IN / Delhi, IN

Experience: 20+ years

Employment Type: Full-time

Role Overview

We are seeking a Principal AI Architect with deep expertise in knowledge creation, semantic modeling, ontology engineering, and reasoning systems. This role is responsible for designing and operationalizing enterprisegrade knowledge architectures using OWL 2, SHACL, RDF, and Azure AI Foundry. You will define the semantic backbone that powers multiagent systems, underwriting intelligence, decision automation, and enterprise knowledge graphs.

This architect will lead the design of ontologies, canonical data models, reasoning pipelines, and agentic workflows, ensuring semantic consistency, explain ability, and interoperability across the enterprise.

Key Responsibilities

1. Ontology & Knowledge Architecture

• Design, author, and maintain enterprise ontologies using OWL 2, including class hierarchies, object/data properties, axioms, and inference rules.
• Develop SHACL shapes for data validation, constraint enforcement, and semantic quality control.
• Build and evolve canonical data models aligned to industry standards (e.g., ACORD for P&C insurance).
• Architect and manage RDF/OWL knowledge graphs supporting reasoning, search, and multiagent workflows.
• Establish governance for ontology lifecycle, versioning, and semantic change management.

2. Reasoning & MultiAgent Systems

• Architect multiagent reasoning frameworks that leverage ontologies for context, grounding, and decision logic.
• Design agenttoagent communication protocols using semantic contracts and shared vocabularies.
• Implement reasoning pipelines using DL reasoners, SHACL engines, and Azure AI Foundry orchestration.
• Integrate symbolic reasoning with LLMbased agents for hybrid neurosymbolic intelligence.

3. AI Platform & Architecture

• Define endtoend architecture for AInative platforms using Azure AI Foundry, Azure Open AI, Azure Functions, Event Grid, and vector/graph stores.
• Build semantic enrichment pipelines for ingestion, mapping, normalization, and validation.
• Partner with engineering teams to implement scalable, productionready knowledge services and APIs.
• Ensure architectures meet enterprise standards for security, compliance, observability, and performance.

4. Data & Semantic Quality

• Establish semantic data quality frameworks using SHACL, SPARQL, and automated validation pipelines.
• Define semantic mapping patterns for structured, semistructured, and unstructured data.
• Lead the creation of knowledge curation workflows for SMEs, underwriters, and data stewards.

5. Leadership & Collaboration

• Serve as the enterprise authority on ontologies, reasoning, and knowledge engineering.
• Mentor engineers, data scientists, and product teams on semantic modeling and AI architecture.
• Collaborate with business stakeholders to translate domain knowledge into formal ontologies and reasoning rules.
• Drive architectural decisions, standards, and best practices across the AI organization.

Required Qualifications

• 8+ years in AI architecture, knowledge engineering, or semantic technologies.
• Expertlevel proficiency in OWL 2, RDF, RDFS, SHACL, and ontology design patterns.
• Handson experience with Protg, SHACL engines, DL reasoners (HermiT, Pellet), and graph databases (Stardog, Neo4j, GraphDB).
• Strong background in Azure AI Foundry, Azure OpenAI, Azure Functions, Event Grid, and cloudnative architectures.
• Deep understanding of knowledge graphs, semantic search, embeddings, and hybrid reasoning.
• Experience designing canonical data models, semantic mappings, and ingestion pipelines.
• Strong ability to translate complex domain knowledge into formal semantic structures.
• Excellent communication skills with the ability to influence senior leadership and engineering teams.

Preferred Qualifications

• Experience with P&C insurance, ACORD standards, underwriting, or risk modeling.
• Background in multiagent systems, agent orchestration, or autonomous workflows.
• Familiarity with LLM grounding, retrievalaugmented reasoning, and neurosymbolic architectures.
• Experience building enterprise knowledge platforms or semantic governance frameworks.
• Publications, patents, or opensource contributions…