Job SummaryKnowledge Graph Architect/ Lead

- Define and design enterprise ontology, taxonomy, and semantic models.
- Architect and implement Knowledge Graph structures including entities, relationships, hierarchies, and semantic mappings.
- Map business entities into graph-based semantic models.
- Design RDF/graph data models, ontology frameworks, and semantic relationships to enable AI-ready contextual understanding.
- Develop and manage graph ingestion, semantic enrichment, lineage, and relationship modeling processes.
- Integrate Knowledge Graph with Data Catalogs, data pipelines, APIs, and AI/ML agents for semantic search and intelligent querying capabilities.
- Enable graph analytics, reasoning, inference, and contextual data discovery using graph technologies and ontology standards.
- Define standards and best practices for semantic modeling, ontology governance, and knowledge graph lifecycle management.
- Ensure semantic layer and graph access follow governance and Data Security Framework controls.
- Support integration using Graph DB technologies, SPARQL/Cypher queries, Java/Python APIs, and semantic web technologies.

Senior Agentic AI Developer (8+ Years Experience)Role Summary

We are seeking a highly experienced Senior Agentic AI Developer and this role focuses on the development and productionization of autonomous, task-oriented AI agents designed to optimize internal engineering workflows, automate complex problem-solving, and enhance platform efficiency.

Required Qualifications

- Experience: Minimum 8 years of skilled software engineering experience with a proven track record in full-stack or backend development.
- Core Technical Stack: Deep practical knowledge of Java and Python, demonstrated through significant development experience.
- Generative AI Expertise: Hands-on experience with LLMs, specifically the Gemini family,



and their application in agentic workflows, reasoning tasks, and tool-calling.
- Agentic Frameworks: Familiarity with Agent Development Kit (ADK), Model Context Protocol (MCP), and agent design in Vertex AI.
- Cloud Infrastructure: Strong understanding of cloud-native development on GCP
- Leadership: Demonstrated ability to lead complex projects from requirement gathering to production release, acting as a strategic liaison between engineering and product stakeholders.

Additional Skills (Good to Have)

- Experience in Google-proprietary technologies such as Borg, Blaze, Piper, and Critique.
- Cloud-native development on GCP, including experience with Spanner, BigQuery, and Pub/Sub.

Key Responsibilities

- Agentic Workflow Design: Architect and orchestrate autonomous agents capable of planning, executing, and verifying complex engineering tasks such as code generation, unit test case creation, and automated bug triaging.
- Platform Integration: Develop and maintain Model Context Protocol (MCP) servers to connect agents to canonical internal documentation and tools, ensuring high-fidelity, grounded responses.
- Tooling & Automation: Utilize the Google Antigravity platform and Agent Development Kit (ADK) to build context-aware assistants that integrate with existing IDEs and development tools.
- Productionization: Lead the transition of AI Proof-of-Concept (POC) projects into production-ready services, collaborating with Moma and other internal search and productivity teams.
- Architectural Leadership: Propose and implement architectural changes to agentic systems to improve reasoning capabilities, reduce latency, and optimize token usage.
- Technical Mentorship: Guide junior engineers in adopting "vibe coding" practices and leveraging Gemini-powered agentic tools for effective software development.

Skill RequirementsOther Requirements

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