# Staff Software Engineer - AI Governance

**Company:** [Rippling](https://hotfix.jobs/companies/rippling)
**Location:** Bengaluru, India
**Role:** Backend Engineering
**Experience:** 8+ years
**Skills:** Backend Engineering, Distributed Systems, Mcp, Access Control, Authorization, Identity Systems, Llm Gateways, Audit Logs, Policy Enforcement, Data Protection, AI Infrastructure, Reliability, Security Engineering
**Posted:** 2026-07-27

> 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.

## 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 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.

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