Job Description
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Company Overview
The hiring company develops infrastructure that enables AI agents to interact with workplace applications and enterprise software. Its engineering team focuses on solving complex challenges involving API integrations, context management, search, and agent-tool interactions. The organization works closely with enterprise customers to advance the capabilities of AI agents and turn emerging research problems into reliable, production-ready solutions.
Responsibilities
- Own enterprise customer engagements from initial discovery and technical specification through implementation, quality assurance, and release.
- Develop and maintain production code within shared toolkits, monorepositories, and customer SDKs.
- Design custom and composite tools, modify schemas and access scopes, and implement customer-specific integrations.
- Transform large enterprise APIs into structured, agent-accessible tool interfaces that AI models can navigate effectively.
- Develop evaluation frameworks to determine whether AI agents select and use the appropriate tools.
- Test and validate integration toolkits, creating repeatable quality assurance processes and evaluation harnesses.
- Conduct regular customer meetings and provide clear summaries, delivery estimates, and escalation updates.
- Manage customer-specific tool adjustments through scalable, self-service workflows rather than relying on repeated manual escalations.
- Collaborate directly with customer engineers to understand requirements, resolve technical issues, and improve integration outcomes.
- Translate real-world integration challenges and research findings into improvements to the broader product.
- Use the company’s own tools in everyday workflows to identify opportunities for improvement.
Qualifications
- Strong applied integration engineering experience, with the ability to deliver production-ready software rather than prototypes alone.
- Experience working extensively with enterprise APIs and authentication systems, including OAuth 2.0, consent models, permission scopes, and multi-workspace tokens.
- Ability to identify authentication and integration edge cases and design robust solutions for them.
- Confidence in making architectural decisions under tight deadlines and explaining technical choices to senior customer engineers.
- Experience taking ownership of enterprise customer engagements from initial discussions through successful deployment.
- Ability to translate ambiguous customer requests into clear technical specifications and challenge requirements when necessary.
- Hands-on experience building applications with large language models.
- Understanding of how to design interfaces and tools specifically for language models and AI agents.
- Strong judgment in designing intuitive developer experiences.
- Excellent written and verbal communication skills, including the ability to explain complex technical concepts clearly under time constraints.
- Ability to build trust with customers and teammates, communicate limitations honestly, and acknowledge uncertainty when appropriate.
Preferred Qualifications
- Production experience with Python; TypeScript experience is also valuable.
- Background in API platforms, integration-platform-as-a-service (iPaaS), or integration-heavy engineering.
- Previous experience as a Forward Deployed Engineer or Solutions Architect working with large technology companies.
- Contributions to emerging AI agent tooling, such as Model Context Protocol (MCP) servers, tool-use evaluation frameworks, or technical publications on agent-tool interactions.
Additional Information
The role addresses emerging challenges in AI agent infrastructure, particularly how models discover, select, and interact with enterprise tools. The position combines customer-facing technical ownership with hands-on engineering and applied research, with an emphasis on turning unresolved technical questions into practical production capabilities.
Disclaimer
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