Senior Manager of AI Engineering (Agent OS Platform)

About the role

Join our team as a Senior Manager of AI Engineering. In this hands-on builder-manager role, you will lead a compact AI platform engineering team responsible for foundational Agent OS capabilities. You will shape architecture, make high-quality technical decisions, and stay close to the work to unblock design, implementation, debugging, evaluation, and production delivery. This is not a pure people-management or AI strategy role; you will earn credibility with senior engineers by improving the technical work. Key missions: Lead a small, hands-on AI platform team in building the core Agent OS, making high-quality technical decisions and unblocking design and implementation challenges.. Oversee the architecture, implementation, production launch, and fast iteration of foundational Agent OS capabilities, including agent runtime, context and memory systems, and evaluation harness.. Collaborate with cross-functional teams to create useful, safe, and measurable agent capabilities, driving evaluation as part of everyday engineering. Profile: - We do not expect one person to have built every part of an agent platform before. We do expect strong engineering judgment, production scars, hands-on curiosity, and the ability to learn fast while making high-quality technical decisions - Strong instincts for production agent safety: typed tools, scoped permissions, business invariants, precondition checks, approval thresholds, reversible actions, idempotency, audit trails, and rollback - Production-minded approach to evaluation: scenario design, behavioral evals, regression suites, trace review, simulation, offline/online metrics, and monitoring for non-deterministic systems - Clear communication with engineers, product leaders, architects, security partners, and executives - Experience shipping AI, ML, data, platform, infrastructure, workflow, automation, or developer-platform systems in production - Strong engineering judgment across APIs, distributed systems, event-driven systems, data platforms, observability, reliability, security, and multi-tenant SaaS constraints - Strong data and context instincts: SQL, unstructured data, vector search, metadata, provenance, source authority, freshness, and privacy boundaries - 8+ years of software engineering experience, including 4+ years leading engineering teams or major technical initiatives in a product or platform organization - Strong technical background as a builder. You may not write production code every day, but you can read code, review implementation plans, reason through distributed systems, and debug real behavior - Recent hands-on technical leadership: you have personally reviewed design docs, read implementation details, inspected production traces/logs, or debugged system behavior in the last 6–12 months - Low-ego coaching style. You raise the technical bar while helping the team move faster - Practical understanding of modern LLM application architecture: model gateways, prompt/context assembly, retrieval, tool calling, structured outputs, memory, agent workflows, and human approval patterns - Ability to turn ambiguous strategy into sequenced roadmaps, measurable outcomes, and clear ownership - Experience with approval-gated automation, compliance-sensitive workflows, audit trails, policy engines, or governed writes to systems of record - Experience building or operating agent runtimes, workflow engines, evaluation platforms, model gateways, ML platforms, developer platforms, or internal control planes - Background in SaaS, vertical software, field service, fintech, ERP, CRM, marketplace, operations, or other domains where software decisions affect real-world business outcomes - Experience integrating AI systems into complex enterprise products where permissions, tenant boundaries, data freshness, customer trust, and reliability are first-order concerns - If you have the skills but are hesitant to apply because of your background, apply anyway