M1 - AI Engineering Lead

Spin - Job Board External; México; On-site; MXN 2,000,000 - 3,000,000; Full time

Job summary

Spin is seeking an AI Engineering Lead to steer a team of AI Engineers focused on building, deploying, and operating production-grade AI agents across Spin’s multi-business ecosystem.

You will combine hands-on engineering with leadership, owning the team’s roadmap and collaborating with product, design, data science, and business stakeholders from discovery through launch.

Qualifications

• Proven experience leading engineering teams while staying technically hands-on
• Shipped and operated an AI agent or agentic system serving real users
• Experience with agent frameworks and LLM APIs
• Strong cross-functional collaboration with product, design, data science and business teams
• Experience in agile, multidisciplinary environments and clear communication

Responsibilities

• Lead, coach, and grow a team of AI Engineers
• Own the delivery of your team’s AI agents roadmap
• Set the technical direction with Staff AI Engineers
• Be accountable for production reliability and post-mortems
• Set and uphold the quality bar for agentic systems
• Review architecture and critical code and unblock your team
• Partner with product managers, designers, data scientists, and business teams
• Manage capacity, headcount planning, and cloud/model cost (FinOps)
• Hire and onboard: define profiles and calibrate the technical bar

Skills

Leadership

Hands-on engineering

Agent frameworks

LLM APIs

Vector databases

Eval frameworks

Distributed systems

Agile methods

Tools

LangChain

LangGraph

OpenAI

Claude

Gemini

Databricks

Job description

Objective

We are seeking an AI Engineering Lead to join our AI Products Chapter and lead a team of AI Engineers that builds, deploys, and operates production-grade AI agents and agentic systems across Spin’s multi-business ecosystem. This is a technical people‑leadership role: you combine the credibility of a hands‑on engineer, comparable to Staff level, with accountability for delivery, quality, and the growth of your team. You partner with Product, Design, Data Science, and business leaders to turn the AI roadmap into reliable products.

• Lead, coach, and grow a team of AI Engineers (up to IC4): onboarding, performance check‑ins, feedback, and career development within Spin's career framework.
• Own the delivery of your team’s AI agents roadmap: prioritize with Product Managers, plan, sequence, and commit to realistic outcomes.
• Set the technical direction with the Staff AI Engineers: architecture reviews, engineering standards, and build‑vs‑buy decisions (framework, model provider, retrieval, orchestration).
• Be accountable for production reliability: on‑call readiness, incident response, post‑mortems, and root‑cause fixes.
• Set and uphold the quality bar for agentic systems: evals, guardrails, observability, and monitoring.
• Review architecture and critical code, and stay close enough to the technology to challenge and unblock your team.
• Partner with Product Managers, Designers, Data Scientists, and business teams from early discovery through launch and iteration.
• Manage capacity, headcount planning inputs, and cost of cloud and model usage (FinOps) for your team.
• Remove blockers, align stakeholders, and communicate status, risks, and trade‑offs clearly to leadership.
• Hire and onboard: define profiles, run interviews, and calibrate the technical bar with Talent.
• Promote an AI‑First culture, knowledge sharing, and responsible AI practices across the chapter.

What we’re looking for

• Seniority: proven experience leading engineering teams while staying technically hands‑on (M1 level), with technical depth comparable to IC5/Staff.
• Evidence of production, not proofs of concept: you have shipped and operated an AI agent, copilot, or agentic system serving real users, and can speak concretely about what broke, how you found out, and how you fixed it.
• People leadership: you have managed, coached, and grown engineers up to Sr level, handled performance conversations, and built a healthy team culture.
• Hands‑on experience with agent frameworks/orchestrators (e.g., LangGraph, LangChain, or equivalent), LLM provider APIs (Claude, OpenAI, Gemini), vector databases, and eval frameworks.
• A strong software engineering foundation: you can review code and architecture and reason about an agent as a distributed system with failure modes, not just a model call.
• Judgment on trade‑offs particular to agentic systems: latency vs. quality, autonomy vs. control, cost vs. accuracy.
• Delivery ownership: planning, prioritization, and stakeholder alignment without needing a dedicated Technical Program Manager.
• Ability to influence cross‑functional decisions with Product, Design, and business stakeholders.
• Experience working in agile, collaborative, multidisciplinary environments; clear communication with technical and business audiences.

We are very excited if you also...

• Experience with Databricks, AWS, or MLflow in production environments.
• Experience designing or leading evaluation and guardrail systems for LLM‑based products.
• Experience hiring across countries and in regulated sectors (banking / fintech), or with financial products, operations, support, growth, or loyalty.
• Interest in human‑in‑the‑loop systems, AI trust, explainability, and responsible AI.

What will you find?

• A company building AI‑First capabilities at scale.
• An AI Products Chapter focused on creating agents, copilots, and intelligent experiences for multiple tribes and squads.
• Real, high‑impact challenges affecting clients, businesses, operations, and the ecosystem.
• Space to experiment, propose, and build new interaction patterns.
• Multidisciplinary teams with a focus on product, data, technology, and user experience.
• A clear growth path within the Y career framework (management track).