Senior Architect – Ontology / Knowledge Graph

Position Overview

We are seeking a highly experienced Senior Architect – Ontology / Knowledge Graph with deep expertise in Ontology, Knowledge Graphs, Semantic Technologies, Azure, Data Architecture, and AI-enabled solutions.

The ideal candidate will have a strong background in enterprise architecture and modern data platforms, with proven experience designing scalable semantic and knowledge-driven architectures for complex enterprise environments. Experience with Stardog, Databricks, healthcare/life sciences, and agentic AI development ecosystems is highly desirable.

The architect will play a key role in defining enterprise architecture, establishing governance standards, and enabling AI-augmented and agentic development workflows across complex, distributed systems.

Key Responsibilities

• Design and govern enterprise-scale ontology, semantic, and knowledge graph platforms.
• Define architecture for AI-enabled, data-driven, and semantic solutions using modern cloud platforms.
• Develop enterprise architecture strategies covering Knowledge Graphs, Ontologies, Semantic Technologies, Azure, and modern data platforms.
• Design cloud-native applications, APIs, integrations, microservices, and distributed systems.
• Define architectural standards and technical roadmaps across Product, Engineering, Security, Data, and Business teams.
• Lead modernization of legacy applications while maintaining business continuity and operational stability.
• Architect solutions that support AI-augmented and agentic development workflows.
• Define architectural intent and standards that autonomous coding agents can follow.
• Break complex features and technical initiatives into agent-executable tasks.
• Establish governance, permissions, guardrails, review processes, and quality checks for AI-generated code.
• Integrate agentic workflows into CI/CD pipelines and SDLC processes.
• Guide the use of agentic IDEs across complex multi-service applications, legacy modernization initiatives, and large codebases/monorepos.
• Evaluate the impact of AI agents and agentic IDEs on SDLC, CI/CD, security, compliance, technical debt, and software quality.
• Establish security and governance practices for autonomous code execution.
• Ensure auditability, traceability, compliance, and security of AI-assisted development workflows.
• Mentor engineering teams on balancing AI autonomy with correctness, maintainability, security, and quality.
• Facilitate architecture discussions and communicate complex technical concepts effectively to executive and technical stakeholders.

Required Skills & Experience

• 12+ years of overall IT/software engineering experience.
• 10+ years of progressive experience in software engineering, solution architecture, or enterprise application development.
• 5+ years of experience leading architecture for enterprise applications or complex technology solutions.
• Strong expertise in:
• Ontology
• Knowledge Graphs
• Semantic Technologies
• Semantic Web
• Data Architecture
• Enterprise Data Platforms
• Strong experience with Azure / Microsoft Azure.
• Experience designing cloud-native applications, APIs, integrations, and distributed systems.
• Experience designing AI-enabled or data-driven solutions using modern cloud platforms.
• Strong understanding of enterprise architecture, application modernization, and technology governance.
• Proven ability to influence architecture decisions across Product, Engineering, Security, Data, and Business stakeholders.
• Strong executive communication, facilitation, collaboration, and relationship-management skills.

Agentic AI / AI-Assisted Development

Strong experience or understanding of agentic development environments and AI-assisted software engineering, including:

• Agentic IDEs and autonomous coding agents.
• Defining architectural intent that AI agents can execute.
• Breaking complex development requirements into agent-executable tasks.
• Establishing guardrails and governance for AI autonomy.
• Integrating AI agents into CI/CD pipelines.
• Supervising AI agents across:
• Multi-service systems
• Large codebases / monorepos
• Legacy modernization
• Complex enterprise applications
• Understanding security implications of autonomous code execution.
• Compliance, auditability, and traceability of AI-assisted development.
• AI-assisted SDLC operating models.
• Establishing code-review practices and quality controls for AI-generated code.

Preferred / Nice-to-Have Skills

• Stardog
• Databricks
• RDF
• OWL
• SPARQL
• Semantic Web technologies
• Healthcare / Healthcare IT
• Life Sciences
• Knowledge Graph platforms
• AI/ML platforms
• Generative AI / Agentic AI
• Microservices and distributed architecture
• CI/CD and DevOps
• Enterprise application modernization

Education

Bachelor's or Master's degree in Engineering, Computer Science, Information Technology, or a related field.

Acceptable qualifications include:

• BE / BTech
• ME / MTech
• BSc / MSc

Technical certifications across cloud, data, architecture, or AI technologies are desirable.