Senior Manager of AI Engineering (Agent OS Platform)

Role Overview

Senior Manager of AI Engineering (Agent OS Platform) — hands-on builder-manager leadership for a compact AI platform engineering team.

In this role, you will shape the architecture and make high-quality technical decisions while staying close to engineering execution (design, implementation, debugging, evaluation, and production delivery). This is not a pure people-management or AI strategy role; you will earn credibility by improving the technical work.

Key Missions

• Lead a small, hands-on AI platform team building the core Agent OS capabilities.
• Own the architecture, implementation, production launch, and fast iteration of foundational Agent OS components, including:
  • Agent runtime
  • Context and memory systems
  • Evaluation harness
• Collaborate cross-functionally to create useful, safe, and measurable agent capabilities, with evaluation integrated into everyday engineering.

Responsibilities (What You’ll Drive)

• Technical decision-making and unblocking design/implementation challenges.
• Production-minded delivery: evaluation, monitoring, and iteration for non-deterministic systems.
• Partnering with engineers, product leaders, architects, security partners, and executives to communicate clearly and align on delivery.

Requirements

• Strong engineering judgment and hands-on curiosity; production “scars” and ability to learn fast.
• Production agent safety instincts, including:
  • Typed tools, scoped permissions
  • Business invariants, precondition checks
  • Approval thresholds
  • Reversible actions, idempotency
  • Audit trails and rollback
• Production-minded evaluation experience, including:
  • Scenario design, behavioral evals, regression suites
  • Trace review, simulation
  • Offline/online metrics and monitoring
• Experience shipping AI/ML/data/platform/workflow/dev-platform systems in production.
• Strong judgment across APIs, distributed/event-driven systems, data platforms, observability, reliability, security, and multi-tenant SaaS constraints.
• Data/context instincts including:
  • SQL and unstructured data
  • Vector search
  • Metadata, provenance, source authority, freshness
  • Privacy boundaries
• 8+ years software engineering experience, including 4+ years leading engineering teams or major technical initiatives.
• Recent hands-on technical leadership (e.g., reviewing design docs, reading implementation details, inspecting production traces/logs, debugging in last 6–12 months).
• Low-ego coaching style: raise the technical bar and help the team move faster.
• Practical understanding of modern LLM app architecture: model gateways, prompt/context assembly, retrieval, tool calling, structured outputs, memory, agent workflows, and human approval patterns.

Nice-to-Haves / Additional Signals

• Experience with approval-gated automation, compliance-sensitive workflows, audit trails, policy engines, or governed writes.
• Experience building/operating agent runtimes, workflow engines, evaluation platforms, model gateways, ML/platforms, or internal control planes.
• Experience integrating AI into complex enterprise products (permissions, tenant boundaries, data freshness, reliability, customer trust).
• Background in SaaS/vertical software domains (e.g., fintech, ERP, CRM, marketplace, operations).

Location / Work Style

• Full remote.

How to Apply

• If you have many of the skills but hesitate due to background, you are encouraged to apply.