M1 - AI Engineering Lead

• →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.

• 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.

• 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.

• 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).

If you are excited about leading the team engineering the future of interaction between humans, AI, and digital products, we want to meet you!