AI Platform Context Lead ID84742

AgileEngine, LLC.; Ciudad de Mendoza; Híbrido; ARS 180.802.000 - 241.069.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Professional growth

Competitive USD-based compensation

Exciting projects with Fortune 500 and

Flextime

Descripción de la vacante

AgileEngine seeks a seasoned AI Platform Context Lead to architect and operate the knowledge graph and grounding layer for an enterprise AI ecosystem. You will shape retrieval, governance, and agent integrations across Slack, Google Workspace, Glean, and AI assistants, delivering scalable, secure capabilities with strong cross-team collaboration.

You will partner with SMEs to design, test, and maintain agents, prompts, and workflows, and translate platform strategy into production-ready designs

Formación

• 6+ years of experience in development roles and hands-on Tech Lead experience.
• Designed and owned knowledge graph or enterprise context/retrieval architecture end to end.
• Produced production-quality AI agents, skills, or integrations using MCP, function calling, RAG, and enterprise search.
• Able to distinguish wrong vs. right retrieval and prioritize fixes by business impact.
• Experience with enterprise SaaS platforms and AI tooling such as Slack, Google Workspace, and AI assistants.

Responsabilidades

• Design, curate, and improve the company context and knowledge graph with ongoing baselines and retrieval quality.
• Review agent outputs and turn findings into a prioritized backlog of fixes.
• Maintain cross-team visibility to fix systemic context gaps architecturally.
• 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.
• Translate platform strategy into technical designs and production-ready capabilities.
• Ensure implementations are scalable, secure, and aligned with standards.
• Build and maintain connectors, APIs, and RAG/semantic-search patterns with permissions.
• Develop discovery tooling and question sets for teams to scope new AI use cases.
• Enable teams to curate context through tooling and guidance.
• Establish testing and evaluation practices for agent output reliability and safety.
• Configure, administer, and maintain Slack, Google Workspace/Gemini, Glean, and other AI assistants.
• Apply access-management, monitoring, and change-control practices; troubleshoot issues.
• Implement data retention and sensitive-data controls with security and compliance teams.
• Maintain documentation, runbooks, and vendor coordination.
• Maintain incident-response and service-continuity procedures.
• Drive continuous improvements in platform reliability, search quality, and cost efficiency.

Conocimientos

Tech Lead

Knowledge graph design

AI agent development

SaaS / enterprise platforms

Identity access mgmt

Herramientas

Slack

Google Workspace

Glean

Claude

ChatGPT

Descripción del empleo

WHY JOIN US

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.

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.

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 integration 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.

PERKS AND BENEFITS

• Professional growth: Accelerate your professional journey with mentorship, TechTalks, and personalized growth roadmaps.
• Competitive compensation: We match your ever‑growing skills, talent, and contributions with competitive USD‑based compensation and budgets for education, fitness, and team activities.
• A selection of exciting projects: Join projects with modern solutions development and top‑tier clients that include Fortune 500 enterprises and leading product brands.
• Flextime: Tailor your schedule for an optimal work‑life balance, by having the options of working from home and going to the office – whatever makes you the happiest and most productive.