Staff AI Security Engineer

Veeam Software•San Jose, CA

1d•$293,100 - $544,200

About The Position

Veeam VDC Security Engineering builds and operates the security platform for a multi-cloud (Azure and AWS) SaaS serving regulated industries. This role sits in Platform Security and helps drive AI/LLM security as a discipline. You will help shape how VDC uses large language models safely (defensively, at scale) and how we use them offensively to find and fix real security defects faster than traditional programs allow.

Requirements

10+ years across security and engineering, with recent focus on AI/ML systems in production (LLM deployments, model risk, or AI-driven security tooling)
Hands-on experience shipping controls (Code, infra or data gaurdrails) that operate on LLM inputs and outputs (redaction, filtering, output validation, prompt-injection defense)
Build and tune detection systems that catch PII and secrets (API keys, credentials, personal data) across large, messy datasets, knowing when a regex rule is good enough, when you need a trained classifier, and when only an LLM can catch it, and justifying that choice on cost, latency, and accuracy grounds
Track record of taking AI/security work from concept to production, including designing for developer trust and false-positive management
Strong cloud security fundamentals across Azure and AWS: RBAC, secret management, key handling, network egress controls
Turn ad-hoc "is this LLM use case safe?" questions into a reusable mechanism (a scoring rubric in PR templates, a lint rule, an approval gate) that teams run themselves, instead of a doc they read once or a person they ping
Comfort building and hardening security tooling in Python and Go
Familiarity with regulated-industry evidentiary expectations (SOC 2 Type 2, ISO 27001, FedRAMP, HITRUST) and how AI-generated evidence intersects with them
Hands-on experience with agentic AI development environments (Claude Code, Cursor, GitHub Copilot Enterprise) and their operational security implications
A demonstrable track record of shipping production code (a code portfolio, open-source contributions, or internal build history). This is a hands-on building role, not an advisory one

Nice To Haves

Familiarity with LLM attack techniques (prompt injection, indirect prompt attacks, tool-boundary abuse, model exfiltration) enough to threat-model and build defenses
Background in traditional AppSec or SAST that translates cleanly to LLM-augmented vulnerability finding
Contributions to open-source LLM safety or evaluation projects (Presidio, PromptFoo, Garak, etc.)
Prior work with Azure OpenAI Service enterprise data-handling controls and LLM governance patterns
Experience integrating security tooling into developer workflows without becoming a merge-blocking bottleneck

Responsibilities

Design and ship the data-handling controls (code, filters, and infrastructure guardrails) for VDC's internal AI tooling and the AI features in our product. Redact sensitive data from prompts, model context, logs, and outputs before any of it reaches a third-party model provider
Build and productionize an AI-enhanced vulnerability reduction capability that plugs into CI/CD, surfaces real defects with a low false-positive rate, and either recommends or applies remediation without eroding developer trust
Publish and evolve a self-serve secure-LLM-use pattern for other VDC engineering teams, including input filtering, output validation, provider-tier data classification, and threat-model shortcuts for teams adding AI features to their products
Threat-model internal AI tools for prompt injection, indirect prompt attack surfaces, model exfiltration, and agent-tool boundary weaknesses. Partner with the red team on findings, then build the fixes and guardrails with tool owners
Partner with Compliance to shape auditor-facing evidence for AI-assisted controls, including which evidence formats hold up when the collector was an LLM and where human review must gate disclosure
Contribute the AI-security lens to adjacent Security Engineering programs where LLMs touch the surface area: vulnerability management maturity, supply chain risk reduction, code owners routing, and compliance evidence collection
Set VDC's direction on emerging LLM security tools and internal AI-forward workflows: what to adopt, what to skip, and what to build in-house

Benefits

Unlimited paid time off
12 paid holidays including 4 global VeeaMe Days for self-care and 24 paid volunteer hours annually through Veeam Cares
Paid parental leave: 8 weeks for all parents, 16 weeks for birthing parents
Medical, dental, and vision coverage starting on your first day
Mental health support, therapy sessions, and digital wellness tools via our Employee Assistance Program
401(k) retirement plan with company matching contributions
Fertility, adoption, and surrogacy support through Maven, plus paid volunteer time
AirVet: 24/7 virtual veterinary care at no cost
Legal services, identity protection, and supplemental health insurance options
Tax-advantaged spending accounts for healthcare, dependent care, and commuting
Opportunities to learn and grow through on-demand libraries (LinkedIn Learning, O’Reilly), mentoring, workshops, and learning events like our annual Global Day of Learning
Pay Transparency