Ai Platform Lead & Support Owner — Remote

Nortal; Ciudad de Mendoza; Remote; ARS 185,448,000 - 278,173,000; Full time

Job summary

AgileEngine in Mendoza is seeking an AI Platform Context Lead to build the knowledge layer for an enterprise AI ecosystem. You will design and curate a resilient context and knowledge graph, integrating with MCP, function calling, and RAG across Slack, Google Workspace, Glean, and AI assistants.

Your work improves answer quality through governance and reusable patterns. You will partner with experts to deploy scalable, secure, and production-ready capabilities, ensuring context-driven results

Qualifications

• 6+ years in development roles with hands-on Tech Lead experience.
• Designed or owned end-to-end knowledge graph or enterprise context architecture.
• Built production-quality AI agents, skills, or integrations using MCP, function calling, and RAG.
• Able to identify context gaps and prioritize fixes by business impact.
• Experience with enterprise SaaS platforms and AI tooling (Slack, Google Workspace, etc.).
• Strong systems administration basics and cross-functional teamwork.
• Excellent troubleshooting, technical writing, and stakeholder communication.

Responsibilities

• Design and evolve the knowledge graph and context layer grounding search and agent responses.
• Review outputs to prevent intent misalignment due to missing or wrong context.
• Maintain cross-team visibility to fix systemic context gaps architecturally.
• Collaborate with SMEs to design, build, test, and maintain AI agents and prompts.
• Decide if needs are best solved by skills, workflows, connectors, or knowledge-graph grounding.
• Translate platform strategy into designs and production-ready capabilities.
• Ensure scalability, security, and standards alignment across implementations.
• Build and maintain connectors, APIs, and RAG/semantic-search patterns.
• Develop discovery tooling and question sets for teams to scope new use cases.
• Empower functional groups to curate context via tooling and guidance.

Skills

Tech Lead

Knowledge graph

Production AI agents

Enterprise SaaS

Systems administration

Cross-functional collaboration

Troubleshooting & writing

English (Upper-intermediate)

Tools

MCP

Function calling

RAG

Enterprise search

Slack

Google Workspace

Claude

ChatGPT

Job description

AgileEngine is an Inc. 5000 company that creates award-winning software for Fortune 500 brands and trailblazing startups across 17+ industries. We rank among the leaders in areas like application development and AI/ML, and our people-first culture has earned us multiple Best Place to Work awards.

WHY JOIN US

If you're looking for a place to grow, make an impact, and work with people who care, we'd love to meet you!

ABOUT THE ROLE

We are looking for an AI Platform Context Lead to build the context and knowledge layer behind an enterprise AI ecosystem. The role combines knowledge-graph design, semantic retrieval, RAG, and agent integrations across Slack, Google Workspace, Glean, and AI assistants. You will improve answer quality through evaluation, governance, and reusable self-service patterns.

MUST HAVES

• 6+ years of experience in development roles, with hands-on experience as a Tech Lead .
• Has designed and/or owned a knowledge graph or enterprise context/retrieval architecture end to end , not merely queried one that someone else built.
• Demonstrated experience building production-quality AI agents, skills, or integrations using MCP, function calling, RAG, and enterprise search .
• Can distinguish "wrong retrieval" from "right retrieval, missing context" in an eval trace, and prioritize fixes by business impact.
• Hands-on experience administering or engineering enterprise SaaS, search, or AI platforms (e.g., Slack, Google Workspace, Glean, Claude, ChatGPT).
• Strong systems-administration background, including identity/access management, configuration, monitoring, and incident response.
• Experience working across multiple business functions, with the ability to turn "what should we ask before we start" into a checklist others can use unsupervised.
• Strong troubleshooting, technical writing, and stakeholder-communication skills, with the ability to lead through influence without direct authority.
• Upper-intermediate English level.

WHAT YOU WILL DO

• 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