About Us

At Félix, we are building the indispensable financial companion for Latinos in the US. We combine an AI-powered, conversational-first interface with real-time financial infrastructure to make cross-border money movement as easy as sending a text. Starting with fast, affordable remittances powered by AI and crypto rails, we are expanding into credit, savings, and wallet services to support the complete immigrant financial journey. Our ambition is to deliver a white-glove financial experience with the simplicity of a conversation to a community the traditional financial system has historically overlooked.

We are a hyper-growth Series C company, backed by over $300 million in funding from top-tier integral investors, including Andreessen Horowitz, QED, Castle Island, Switch Ventures, HTwenty, Monashees, General Catalyst Customer Value Fund. This isn‘t just about the numbers; it‘s a testament to the trust our investors have in our vision and our team. Additionally, Félix was selected as an “Endeavor Entrepreneur” and was a recipient of the CrossTech Fintech Startups Award.

Joining Félix means you will be part of a team building a legacy, a company that will outlive us all. This is a rare opportunity to apply your skills to a deeply meaningful mission—serving a community that has been underserved for too long because we are obsessed with our customers. We get things done with urgency and focus, driven by extreme ownership over our impact. We collaborate without ego, fostering radical transparency and fierce loyalty so we can grow together. Because we aim for insanely great instead of just good enough, we stay insatiably curious—always experimenting, building the future today, and delivering a product that truly makes our users' lives better.





About the Role

We’re looking for an Applied AI Engineer to help the AI team and colleagues across Félix get more out of AI in their daily work. You’ll build and debug agents and integrations, help colleagues with their own builds, and turn what works into reusable frameworks and learning resources. Success means teams can use AI on their own, solve technical blockers, and keep their AI workflows running day to day.

You will combine hands-on engineering with technical office hours, mentoring and role-specific learning programs. Working with team leads and Internal Talent, you will identify skill gaps, track applied learning and follow through after training. You 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.

Responsibilities

- Build support: Build and improve reference agents, tool integrations and workflows with Python, LLM SDKs and APIs. Pair with colleagues to scope practical builds, review code, and debug prompts, retrieval, tool calls, state, permissions and failures. Production systems stay with FDEs and specialist engineers.
- Office hours and escalation: Run regular technical office hours, triage requests and track issues to closure. Escalate complex integration,



data, security or production problems with reproducible examples, logs and attempted fixes, and turn recurring issues into resources.
- Reusable frameworks: Turn shared standards into starter kits, tested agents, integration patterns and troubleshooting guides, with examples for tool calling, data access, evaluations, human review and failure handling. Keep documentation, versioning, permissions and ownership clear on approved platforms, including Maestro, our in-house AI assistant that works across Slack, Notion, Google Workspace and our data.
- Technical teaching and mentoring: Own the technical content and exercises for hands-on bootcamps and tracks, and teach through live builds and debugging. Mentor colleagues through guided builds and code reviews until they can maintain their own work.
- Platform expertise and improvement: Keep working expertise in Maestro, other approved tools and data safeguards. Test new capabilities on real tasks, share practical guidance, and bring recurring gaps to platform owners. Track independent task completion, blocker resolution, resource reuse and sustained adoption.

Requirements

- Applied AI

experience:
Hands-on experience building LLM applications, agents or automations for real users, including what failed and how you improved it.
- Engineering and diagnostics: Strong Python, APIs, authentication, Git, testing and debugging, with practical use of at least one LLM SDK such as OpenAI or Google. You can read logs and traces, isolate failures and create reproducible examples.
- Agents and evaluations: Experience with tool calling, multi-step workflows, state, structured outputs, human review and failur

📌 Applied AI Engineer | L&D (Ciudad de México)
🏢 Félix
📍 Ciudad de México