Staff Software Engineer - AI Governance
Leads architecture and implementation of backend systems governing enterprise AI usage, including authorization, policy enforcement, auditability, and spend attribution. The role requires 8+ years of software engineering experience, deep distributed-systems expertise, and strong security and technical leadership judgment.
About the job
Responsibilities
- Lead technical architecture for core AI governance systems, including MCP access control, model gateway policy, runtime authorization, audit pipelines, and usage attribution.
- Design distributed systems that enforce policy at the moment of use without adding unnecessary latency or operational fragility.
- Build identity-aware controls connecting AI usage to organizational graphs, roles, permissions, applications, devices, and workforce data.
- Own ambiguous product and technical problems across shadow AI detection, agent permissions, tool access, spend attribution, and data protection.
- Partner with Product, Security, Legal, IT, and Engineering leaders to turn a new enterprise problem space into durable platform capabilities.
- Set technical direction for a new team, including architecture reviews, design standards, reliability expectations, and long-term system boundaries.
- Mentor senior engineers and raise the engineering bar for secure, observable, and trusted systems.
Requirements
- 8+ years of professional software engineering experience, with a strong track record of technical leadership and organization-wide impact.
- Deep backend and distributed systems expertise, including experience designing reliable services with clear ownership and well-defined interfaces.
- High agency and a bias toward shipping, with the ability to turn ambiguous product ideas into focused launches, fast feedback loops, and durable technical foundations.
- An AI-native engineering mindset, including hands-on use of AI tools and sound judgment about when correctness, security, and architecture require deeper human ownership.
- Strong security and systems judgment, especially around authorization, identity, data movement, audit logs, or policy enforcement.
- Product sense for enterprise software, with a focus on customer-visible impact and operational simplicity.
- Ability to communicate clearly with engineering and non-engineering stakeholders while navigating trade-offs across security, usability, and speed.
Nice-to-haves
- Familiarity with AI infrastructure, LLM gateways, Model Context Protocol (MCP), developer tools, identity systems, or data protection.
Skills
Backend Engineering, Distributed Systems, Mcp, Access Control, Authorization, Identity Systems, Llm Gateways, Audit Logs, Policy Enforcement, Data Protection, AI Infrastructure, Reliability, Security Engineering
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