Senior Software Engineer - AI Governance
Build foundational backend and distributed systems for AI governance, including access control, policy enforcement, runtime authorization, auditability, and usage attribution. The role requires 6+ years of software engineering experience, strong security judgment, and technical leadership.
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 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 develop durable platform capabilities for enterprise AI governance.
- 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, trusted systems.
Requirements
- 6+ 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 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 and durable technical foundations.
- AI-native engineering mindset, including hands-on use of AI tools and sound judgment about correctness, security, and architecture.
- Strong security and systems judgment, particularly around authorization, identity, data movement, audit logs, or policy enforcement.
- Product sense for enterprise software and a focus on customer-visible impact and operational simplicity.
- Clear communication with engineering and non-engineering stakeholders, including the ability to navigate security, usability, and speed trade-offs.
Nice to Have
- Familiarity with AI infrastructure, LLM gateways, MCP, developer tools, identity systems, or data protection.
Skills
Backend Systems, Distributed Systems, Mcp, Access Control, Authorization, Identity Management, Llm Gateways, Audit Logs, Data Protection, Policy Enforcement, AI Infrastructure, Reliability
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