Senior Software Security Engineer

GitLab GitLab · Enterprise · Bangalore, India +4 · Remote · Security Operations

Senior Software Security Engineer at GitLab focused on building and maintaining abuse prevention systems, including agentic AI capabilities and LLM-aided anomaly detection, to mitigate platform abuse on SaaS platforms. Requires strong software engineering background, particularly in Ruby/Rails, and experience with cloud-native development.

What you'd actually do

  1. Maintain core abuse prevention systems and build new abuse detection rules to identify and prevent evolving platform abuse such as spam, AI/token, SEO optimization/redirects and other financially motivated abuse campaigns.
  2. Become a core maintainer for our in-house abuse platform (Ruby on Rails monolith) and be comfortable supporting and building new features for the platform.
  3. Improve and expand agentic AI capabilities in our abuse mitigation tools, including improving multi agent reasoning decision patterns with a target to reduce HITL operational load.
  4. Lead collaboration with peer engineering teams to deliver safety improvements for the GitLab product
  5. Resolve automation gaps and create efficient, automated processes

Skills

Required

  • Strong software development skills with experience in Ruby/Rails
  • Strong experience with cloud native development (Google Cloud Platform (GCP) and/or AWS)
  • Comfortable working in an all remote environment
  • Interest in “thinking like a hacker” and defending against attacks with an “automation first” mindset
  • Interest in handling trust and safety security incidents and collaborating with engineering to harden platform defenses to combat abuse campaigns
  • Experience working on an AI native development team maintaining teams of agents and acting as a code reviewer and experience abstracting your role away from writing code for most cases.

What the JD emphasized

  • strong software engineering background with experience in large Ruby/Rails codebases is required
  • predictively identify abuse patterns and trends and build anomaly detection and prevention systems
  • improving multi agent reasoning decision patterns
  • LLM aided anomaly detections

Other signals

  • improving agentic AI capabilities
  • multi agent reasoning decision patterns
  • LLM aided anomaly detections