AI Developer Relations Engineer (Copy)

Felix Technologies•Seattle, WA

1d•$193,000 - $213,000•Hybrid

About The Position

Félix is a hyper-growth Series C company focused on serving an underserved community. The company describes its culture as customer-obsessed, urgent, collaborative, transparent, and curious. Félix is becoming an AI-native company and is hiring someone to take AI initiatives prioritized by leadership from idea through deployment, adoption, and ownership. The role works across Engineering, Product, Data, FDE, AI Enterprise, and platform teams. Specialist engineers own architecture and implementation; this role owns plans, dependencies, decisions, and follow-through to ensure the intended problem is solved. The role reports to the Head of AI Enablement & Talent Development. The position translates AI priorities into technical plans, coordinates cross-team work and technical decisions, manages delivery risks and blockers, and coordinates evaluation and deployment readiness. It also secures support and maintenance ownership, measures solution outcomes, coordinates early reviews with AISec and the AI Governance Committee, and turns project lessons into reusable standards. The role requires technical program leadership, hands-on technical fluency, knowledge of agent workflows and evaluations, operational rigor, and strong communication and ownership. Preferred experience includes applied LLM work used by real users, RAG and vector databases, integrations into other teams’ environments, regulated or data-sensitive work, and CX, support, or compliance metrics. 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. The role offers a hybrid work setup and the benefits listed in the posting.

Requirements

Proven experience leading complex technical programs or software delivery across several teams, with specific examples of personal judgment, design contributions, decisions, and results.
Practical skills in Python, SQL, APIs, authentication, Git, and testing.
Comfort using at least one LLM SDK, such as OpenAI or Google, to prototype integrations and debug issues.
Understanding of data modeling, pipelines, and enterprise integration patterns.
Experience with tool calling, multi-step orchestration, state, structured outputs, human review, and fallbacks.
Ability to assess quality, latency, cost, and permissions risks and define useful evaluations.
Ability to establish baselines, validate assumptions through experiments, and balance sophistication with operational constraints.
Track record of translating between business and engineering teams, influencing without formal authority, mentoring others, and escalating decisions early.
Examples of solution designs, evaluation decisions, or delivery outcomes; confidential work may be discussed without sharing proprietary material.

Nice To Haves

Applied LLM work used by real users.
RAG and vector database experience.
Experience deploying into environments owned by other teams.
Experience in a regulated or data-sensitive setting such as fintech.
Experience with CX, support, or compliance metrics such as AHT, FCR, CSAT, and SLA.

Responsibilities

Translate AI priorities into technical plans with scope, requirements, milestones, success metrics, acceptance criteria, and named owners.
Turn incomplete briefs into testable hypotheses, baselines, and measurable outcomes.
Coordinate technical dependencies across Engineering, Product, Data, FDE, AI Enterprise, and platform teams.
Drive technical decisions with engineers by challenging assumptions, making trade-offs explicit, and recording outcomes.
Manage execution and risk by tracking commitments, removing blockers, and escalating early with recommendations.
Coordinate evaluations and deployment readiness, including quality, latency, cost, and permissions risks.
Verify that FDE integrations work in the receiving team’s environment.
Secure named owners for support, monitoring, and maintenance, and measure whether each solution solved the intended problem.
Work with AISec and the AI Governance Committee to ensure reviews happen early.
Turn project lessons into reusable briefs, checklists, decision records, and handoff standards.