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.

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/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.

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.
- Growth without limits: build your skills through mentorship, internal TechTalks, challenging projects, and a dedicated annual learning budget
- Competitive compensation: get recognition that reflects your skills and impact, with regular performance and compensation reviews
- Flexibility: work 100% remotely with versátil hours that support focus, autonomy, and a healthy work rhythm
- Meaningful, modern projects: build impactful products using modern technologies alongside global teams an

📌 Artificial Intelligence Engineer (Argentina)
🏢 AgileEngine
📍 Argentina