Product Security Engineer 3

Adobe Adobe · Enterprise · Bangalore, India

Product Security Engineer at Adobe focusing on integrating security into the SDLC, with a specific emphasis on AI/LLM systems. Responsibilities include penetration testing, embedding security controls into CI/CD pipelines, and researching emerging AI/ML exploits.

What you'd actually do

  1. Conduct penetration tests on AI/LLM systems (prompt injection, model poisoning, jailbreaks, etc.), web applications, APIs, mobile apps, cloud infrastructure, containers, and supporting infrastructure.
  2. Embed security controls into CI/CD pipelines: SAST, DAST, SCA, secrets scanning, and container/image scanning as first-class pipeline gates.
  3. Design and operate DevSecOps automation across cloud environments (AWS, Azure, GCP): policy-as-code, infrastructure-as-code scanning, and automated security guardrails.
  4. Research emerging AI/ML exploits, cloud-native attack techniques, and supply chain risks to stay ahead of threats.
  5. Build the feedback loop from security findings back into preventive controls so the same class of bug doesn't ship twice.

Skills

Required

  • penetration testing
  • DevSecOps
  • CI/CD pipeline security integration
  • AI/ML security
  • LLM vulnerabilities
  • OWASP Top 10
  • OWASP API Top 10
  • OWASP LLM Top 10
  • Python
  • Bash
  • PowerShell
  • Go
  • JavaScript
  • cloud security (AWS, Azure, GCP)
  • containers (Docker, Kubernetes)
  • infrastructure-as-code (Terraform, CloudFormation)
  • policy-as-code
  • attack vectors
  • exploit development
  • vulnerability exploitation
  • chained attacks
  • secure coding practices
  • common code-level vulnerabilities
  • written and verbal communication

Nice to have

  • Master's degree in IT, Computer Science, or related fields
  • OSCP
  • OSWE
  • OSEP
  • GXPN
  • GPEN
  • GWAPT
  • CRTP
  • eJPT
  • CREST
  • CISSP
  • Published CVEs
  • Bug bounty experience
  • Capture The Flag (CTF) experience
  • AI/ML security research
  • Advanced exploitation
  • custom tooling development
  • Threat modeling
  • secure DevOps at enterprise scale
  • AI-assisted security tooling
  • RAG
  • agentic workflows
  • Open-source contributions
  • technical writing on offensive security, DevSecOps, or AI security

What the JD emphasized

  • AI/LLM systems
  • penetration testing
  • DevSecOps
  • security guardrails
  • AI/ML security
  • LLM vulnerabilities
  • prompt engineering attacks

Other signals

  • AI/LLM systems security assessments
  • integration of security across SDLC
  • building and operating security guardrails
  • penetration testing expertise
  • DevSecOps background