AI Developer Relations Engineer

Felix Technologies•San Francisco, CA

23h•Hybrid

About The Position

Félix is hiring an Applied AI Engineer to help the AI team and colleagues across the company use AI in their daily work. The role combines hands-on engineering with technical office hours, mentoring, and role-specific learning programs. The successful candidate will build and debug agents and integrations, support colleagues’ projects, and turn effective approaches into reusable frameworks and learning resources so teams can work independently, resolve technical blockers, and maintain AI workflows. The engineer will develop expertise in Maestro and other approved platforms, partner with the AI Solutions Manager on shared practices, and engage FDE, AI Enterprise, Data, Security, and platform specialists when deeper support is needed. Production systems remain the responsibility of FDEs and specialist engineers. Félix describes itself as a hyper-growth Series C company focused on serving an underserved community. It emphasizes customer focus, urgency, ownership, collaboration, transparency, curiosity, and experimentation. The role is hybrid. The starting base salary typically ranges from $193,000 to $213,000 USD, tailored to experience and skill set. Equity is a core component of total compensation.

Requirements

Hands-on experience building LLM applications, agents, or automations for real users, including learning from failures and improving the work.
Strong Python, APIs, authentication, Git, testing, and debugging skills.
Practical experience with at least one LLM SDK, such as OpenAI or Google.
Ability to read logs and traces, isolate failures, and create reproducible examples.
Experience with tool calling, multi-step workflows, state, structured outputs, human review, and failure handling.
Ability to build useful evaluations and explain quality, latency, cost, and data safeguards.
Experience mentoring builders and mixed technical and business audiences through live builds and debugging, with follow-through after training.
Ability to turn ambiguous requests into testable hypotheses, recognize when to escalate, and close the loop.
Pass a live pairing or debugging exercise during the interview.

Nice To Haves

Practical SQL experience.
CI/CD experience.
Experience with RAG and vector databases.
Experience building maintainable starter kits or reusable components.
Experience supporting operations teams.

Responsibilities

Build and improve reference agents, tool integrations, and workflows using Python, LLM SDKs, and APIs.
Pair with colleagues to scope practical builds, review code, and debug prompts, retrieval, tool calls, state, permissions, and failures.
Run regular technical office hours, triage requests, and track issues through resolution.
Escalate complex integration, data, security, or production problems with reproducible examples, logs, and attempted fixes; turn recurring issues into resources.
Create starter kits, tested agents, integration patterns, and troubleshooting guides covering tool calling, data access, evaluations, human review, and failure handling.
Maintain clear documentation, versioning, permissions, and ownership on approved platforms, including Maestro.
Own technical content and exercises for hands-on bootcamps and learning tracks; teach through live builds and debugging.
Mentor colleagues through guided builds and code reviews until they can maintain their own work.
Maintain working expertise in Maestro, other approved tools, and data safeguards; test new capabilities on real tasks and share practical guidance.
Bring recurring platform gaps to platform owners.
Track independent task completion, blocker resolution, resource reuse, and sustained adoption.
Work with team leads and Internal Talent to identify skill gaps, track applied learning, and follow through after training.
Partner with the AI Solutions Manager on shared practices and engage FDE, AI Enterprise, Data, Security, and platform specialists when needed.