AI Platform Context Lead ID84742

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.

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 - You must be authorized to work for ANY employer in the US (e.g., Green card holders, TN visa holders, GC EAD, H4 EAD, U4U with EAD), as we are unable to sponsor or take over employment visa sponsorship at this time;

- 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…