# Senior Software Engineer - AI Governance

**Company:** [Rippling](https://hotfix.jobs/companies/rippling)
**Location:** Bengaluru, India
**Role:** Backend Engineering
**Experience:** 6+ years
**Skills:** Backend Systems, Distributed Systems, Mcp, Access Control, Authorization, Identity Management, Llm Gateways, Audit Logs, Data Protection, Policy Enforcement, AI Infrastructure, Reliability
**Posted:** 2026-07-15

> 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.

## Job Description

## 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.

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