AI Context & Knowledge Graph Architect

AgileEngine, LLC.

Houston (TX)

On-site

USD 150,000 - 230,000

Full time

4 days ago

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Benefits offered by this job

Professional growth

Competitive compensation

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Flextime

Job summary

AgileEngine, LLC. is seeking an AI Platform Context Lead to shape the knowledge layer behind an enterprise AI ecosystem. You will design and improve the context, maintain a scalable knowledge graph, and partner with SMEs to build AI agents, skills, prompts, and workflows.

This role focuses on retrieval quality, governance, and production-ready capabilities. You will evaluate platform capabilities, ensure secure, scalable implementations, and drive continuous improvements across reliability and

Qualifications

6+ years of experience in development roles with hands-on Tech Lead work.
Designed or owned a knowledge graph or enterprise context/retrieval architecture end to end.
Built production AI agents, skills, or integrations using MCP, function calling, RAG, and enterprise search.
Able to distinguish wrong retrieval from missing context and prioritize fixes by business impact.
Experience administering or engineering enterprise SaaS/AI platforms (Slack, Google Workspace, Glean, Claude, ChatGPT).
Strong identity/access management, monitoring, and incident response capabilities.

Responsibilities

Design, curate, and improve the knowledge graph grounding search and agent responses with measurable accuracy.
Review agent outputs to identify intent gaps and create a prioritized backlog for fixes.
Maintain cross-team visibility to ensure systemic context gaps are resolved architecturally.
Partner with SMEs to design, test, and maintain AI agents, prompts, and workflows.
Decide whether skills, workflows, connectors, or knowledge-graph grounding are best for a need.
Translate platform strategy into designs and plans; scale pilots into production capabilities.
Ensure implementations are scalable, secure, and aligned with standards.
Build connectors, APIs, and RAG/semantic-search patterns while preserving permissions.
Develop discovery tooling and question sets for other teams to scope new AI use cases.
Empower functional groups to curate context via tooling and guidance rather than mandates.
Establish testing and evaluation practices for agent output quality and safety.
Configure, administer, and maintain Slack, Google Workspace/Gemini, Glean, and AI assistants.
Apply access-management, monitoring, and change-control best practices; assist security teams.
Maintain documentation, runbooks, and vendor coordination; manage incident-response plans.
Drive continuous improvement in platform reliability, search quality, and cost efficiency.