Enterprise AI Knowledge Graph Architect

AgileEngine, LLC.; West Palm Beach (FL); On-site; USD 170,000 - 210,000; Full time

Benefits offered by this job

Professional growth

Competitive compensation

A selection of exciting projects

Flextime

Job summary

AgileEngine, LLC. is seeking an AI Platform Context Lead to build the context and knowledge layer behind an enterprise AI ecosystem. You will design, curate, and maintain the knowledge graph, enable semantic retrieval, and integrate with Slack, Google Workspace, Glean, and AI assistants.

Collaborate with subject-matter experts to deploy agents and workflows, assess retrieval quality, and establish scalable, secure patterns while documenting runbooks and governance practices.

Qualifications

• Must have authorization to work in the US without sponsorship.
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Responsibilities

• Design, curate, and continuously improve the company context and knowledge graph that grounds search and agent responses, managing schema, coverage, freshness, and retrieval quality as an ongoing responsibility rather than a one-time build, with a stated accuracy and efficiency baseline that improves over time.
• Review agent and skill outputs to catch cases where a result missed user intent because the underlying context was missing or wrong, and turn those findings into a prioritized backlog of fixes so that intent-breaking context gaps are resolved rather than left to resurface.
• Maintain enough cross-team visibility that a systemic context gap gets fixed once, architecturally, instead of being patched repeatedly.
• Partner with subject-matter experts to design, build, test, and maintain AI agents, skills, prompts, and workflows using MCP, function calling, and comparable frameworks.
• Decide whether a need is best solved by a skill, workflow, connector, or knowledge-graph grounding, based on where the logic belongs rather than what is fastest to ship.
• Translate platform strategy into technical designs, integrations, and implementation plans, and turn pilots into reliable, production-ready capabilities by evaluating platform capabilities.
• Ensure implementations are scalable, secure, and aligned with organizational standards.
• Build and maintain connectors, APIs, and RAG/semantic-search patterns while preserving source-system permissions.
• Build and maintain the discovery tooling and question sets other teams use to scope new AI use cases on their own, reducing the hands-on involvement required from this role.
• Enable functional groups to curate and maintain their own context in the way that works best for them through tooling, patterns, and guidance, rather than one-size-fits-all mandates.
• Establish testing and evaluation practices for agent output quality, reliability, and safety.
• Configure, administer, and maintain Slack, Google Workspace/Gemini, Glean, and other AI assistants.
• Apply access-management, monitoring, and change-control best practices, and troubleshoot complex platform issues.
• Implement technical controls for access, data retention, and sensitive-data handling in partnership with security, privacy, and compliance teams.
• Flag connectors needing elevated or write access early, during design, and route them to security for review.
• Maintain technical documentation, runbooks, and vendor coordination.
• Maintain incident-response and service-continuity procedures.
• Drive continuous improvement in platform reliability, search quality, and cost efficiency over time.

Skills

Knowledge graph design

Enterprise AI platforms

Slack & Google Workspace integration

Security & IAM

Cross-functional collaboration

English proficiency

MCP

Function calling

RAG

Enterprise search

Tools

MCP

Function calling

RAG

Enterprise search

Job description

AgileEngine, LLC. is seeking an AI Platform Context Lead to build the context and knowledge layer behind an enterprise AI ecosystem. You will design, curate, and maintain the knowledge graph, enable semantic retrieval, and integrate with Slack, Google Workspace, Glean, and AI assistants.

Collaborate with subject-matter experts to deploy agents and workflows, assess retrieval quality, and establish scalable, secure patterns while documenting runbooks and governance practices.