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.
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