AI Platform Context Lead ID84742

AgileEngine; Cartagena de Indias; À distance; COP 348 068 000 - 601 209 000; Plein temps

Avantages offerts par ce poste

Professional growth

Competitive compensation (USD-based)

Exciting projects

Flextime

Résumé du poste

AgileEngine is seeking an AI Platform Context Lead to build the 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. You will partner with SMEs to design, build, test, and maintain AI agents, skills, prompts, and workflows, using MCP, function calling,

Qualifications

• 6+ years in development roles with hands-on Tech Lead experience.
• Designed or owned an end-to-end knowledge graph or enterprise context/retrieval architecture.
• Experience building 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.
• Hands-on experience with enterprise SaaS, search, or AI platforms (Slack, Google Workspace, Glean, Claude, ChatGPT).
• Strong systems-administration background incl. identity/access management, configuration, monitoring, and incident response.
• Experience turning asks into reusable checklists for cross-functional teams.
• Strong troubleshooting, technical writing, and stakeholder communication; ability to lead through influence.

Responsabilités

• Design, curate, and improve the company context and knowledge graph with accuracy baselines.
• Review agent outputs to identify context gaps and backlog fixes.
• Maintain cross-team visibility to fix systemic context gaps architecturally.
• Partner with SMEs to design, build, test, and maintain AI agents, skills, prompts, and workflows.
• Decide whether a need is best solved by a skill, workflow, connector, or knowledge-graph grounding.
• Translate platform strategy into designs, integrations, and production plans.
• Ensure implementations are scalable and secure and aligned with standards.
• Build and maintain connectors, APIs, and RAG/semantic-search patterns with permissions intact.
• Create discovery tooling and question sets for self-serve AI use cases.
• Enable functional groups to curate their own context through tooling and guidance.
• Establish testing and evaluation practices for agent output quality and safety.
• Configure, administer, and maintain Slack, Google Workspace/Gemini, Glean, and other AI assistants.
• Apply access-management and monitoring best practices; troubleshoot complex platform 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 improvement in platform reliability, search quality, and cost efficiency.

Connaissances

Tech Lead

Knowledge graph

Production‑quality AI

Evaluation governance

SaaS platforms

Identity/access management

Cross-functional collaboration

English (Upper‑intermediate)

Outils

Slack

Google Workspace

Glean

Description du poste

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/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.

PERKS AND BENEFITS

• Professional growth: Mentorship, TechTalks, and personalized growth roadmaps.
• Competitive compensation: USD-based pay with education, fitness, and team activity budgets.
• Exciting projects: Modern solutions with Fortune 500 and top product companies.
• Flextime: Flexible schedule with remote and office options.