AI Security Engineer

Euna Solutions•Oakville, ON

1d•CA$175,000 - CA$215,000•Hybrid

About The Position

AI is reshaping how software gets built — and the security challenges that come with it are genuinely new territory. At Euna Solutions, we're looking for an Security Engineer who's ready to plant a flag at that frontier: someone with deep security fundamentals who's started turning their attention to AI/ML systems and is hungry to go further. If you know the landscape cold and you're energized by threat vectors that didn't exist five years ago, this is where your next chapter gets interesting.

Requirements

5–8 years in security engineering, DevSecOps, or a related discipline — with recent hands-on exposure to AI/ML environments
Solid foundation across multiple security domains: application security, cloud security, network security, and/or DevSecOps
Working familiarity with AI/ML security frameworks — OWASP LLM Top 10, MITRE ATLAS, NIST AI RMF, EU AI Act — and what problems they're actually solving for
Awareness of agentic AI architectures and the risks they introduce: autonomous agents, tool orchestration, memory systems, multi-agent coordination
Proficiency in Python and at least one additional scripting language (Ruby or Shell), with experience in REST API or GraphQL integrations
Hands-on experience with at least one major cloud provider (AWS, Azure, or GCP) and comfort working cross-functionally in an agile environment

Nice To Haves

Direct experience with LLM security, model red-teaming, or AI threat modeling
Hands-on work securing agentic AI systems using frameworks like LangChain, AutoGen, or CrewAI
Experience contributing to AI governance programs, risk registers, or responsible AI policies
CI/CD pipeline development with AI/ML workloads, or containerization experience (Docker, Kubernetes, Helm)

Responsibilities

Assess and mitigate AI/ML-specific security risks — model integrity, data poisoning, prompt injection, adversarial attacks, and agentic threat vectors
Implement and maintain security tooling, automation, and processes across AI/ML pipelines and infrastructure
Define and enforce secure AI development practices across the full SDLC
Evaluate and secure agentic AI systems, including multi-agent architectures, tool-use frameworks, and autonomous decision-making pipelines
Contribute to AI governance initiatives — policy development, risk assessments, and responsible AI frameworks aligned with regulatory and industry standards
Embed AI security controls cross-functionally across engineering and operations teams
Own AI security projects end-to-end, from conception through delivery