AI Platform Context Lead: Knowledge Graph & RAG

Agileengine; Victoria; Hybrid; ARS 181,094,000 - 271,641,000; Full time

Benefits offered by this job

Professional growth

Competitive USD-based compensation

A selection of exciting projects

Flextime

Job summary

AgileEngine is hiring an AI Platform Context Lead to architect the knowledge layer for an enterprise AI ecosystem. You will design and govern the knowledge graph, grounding search and agent responses, and guide cross-team improvements.

Lead AI agents and integrations using MCP, function calling, and RAG, align with security and compliance, and scale platform reliability while delivering cost-efficient solutions.

Qualifications

• 6+ years in development roles with hands-on leadership as a Tech Lead.
• Experience designing/owning a knowledge graph or enterprise context/retrieval architecture end to end.
• Production-quality AI agents, skills, or integrations using MCP, function calling, RAG, and enterprise search.

Responsibilities

• Design, curate, and continuously improve the knowledge graph grounding search and agent responses, with measurable accuracy and efficiency baselines.
• Review agent outputs to identify missed context and create a backlog of fixes to close context gaps.
• Maintain cross-team visibility to fix systemic context gaps architecturally.
• Partner with subject-matter experts to design, build, test, and maintain AI agents, skills, prompts, and workflows.
• Decide whether needs are best solved by a skill, workflow, connector, or knowledge-graph grounding.

Skills

6+ years development

Knowledge graph architecture

AI agents MCP RAG

Enterprise SaaS platforms

Identity & access mgmt

Cross-functional collaboration

Technical writing

Tools

Slack

Google Workspace

Glean

Claude

ChatGPT

Job description

AgileEngine is hiring an AI Platform Context Lead to architect the knowledge layer for an enterprise AI ecosystem. You will design and govern the knowledge graph, grounding search and agent responses, and guide cross-team improvements.

Lead AI agents and integrations using MCP, function calling, and RAG, align with security and compliance, and scale platform reliability while delivering cost-efficient solutions.