Senior Backend Engineer, Sscs: AI Governance

GitLab GitLab · Enterprise · India · Sec Engineering

Senior Backend Engineer focused on building backend systems for an AI Governance product. This role involves implementing pipelines for AI audit events, access control for AI features, storage for AI agent artifacts, and services for an AI agent registry. The work is at the intersection of AI, governance, and enterprise backend engineering, supporting regulated organizations in adopting AI agents with confidence.

What you'd actually do

  1. Implement and evolve the AI audit event pipeline, including event ingestion, schema normalization, storage design, partitioning, retention, and export capabilities.
  2. Implement access control for AI Governance features by integrating permissions for audit logs, policy configuration, and governance dashboards into GitLab's existing authorization model.
  3. Contribute backend functionality for the AI agent artifact feature, supporting structured storage and retrieval of agent run metadata alongside existing CI/CD artifacts.
  4. Build backend services for the MCP registry, including tool metadata and enforcement controls that can restrict or block access when needed.
  5. Design and optimize data models and queries for high-write, event-heavy systems using PostgreSQL and ClickHouse.

Skills

Required

  • Extensive experience building backend applications with Ruby on Rails in production environments.
  • Proficiency in Python and experience building backend services that support AI infrastructure, gateways, or adjacent product systems.
  • Extensive experience with PostgreSQL and other data-intensive databases such as ClickHouse, including schema design, partitioning strategies, and efficient query patterns for event-heavy workloads.
  • Experience building REST or GraphQL APIs and designing backend systems for reliable storage, retrieval, and governance workflows.
  • Solid understanding of authorization, access control, and enterprise governance concepts in web application architectures.
  • Familiarity with regulatory compliance, auditability, or enterprise governance requirements, and the ability to apply them in production systems.
  • Clear written communication skills and comfort working effectively in a remote, async-first team.

Nice to have

  • audit trails
  • telemetry
  • event streaming
  • SIEM integrations
  • webhook delivery
  • enterprise retention requirements
  • AI agent infrastructure
  • large language models
  • the Model Context Protocol (MCP)
  • compliance, observability, and enterprise security products

What the JD emphasized

  • backend systems behind a paid product for regulated enterprise organizations
  • clear visibility, policy controls, and compliance evidence for AI use inside the software development lifecycle
  • customers adopt AI agents with more confidence
  • enterprise backend engineering
  • product requirements are shaped by emerging AI regulations and customer governance needs
  • manage AI usage with the same rigor they apply to security, compliance, and software delivery

Other signals

  • AI Governance
  • backend systems for AI
  • regulated enterprise organizations
  • AI use inside the software development lifecycle
  • AI agents