The Senior AI & Machine Learning Security Engineer is at the forefront of the next frontier in cybersecurity. You will be responsible for ensuring that our Client’s AI/ML initiatives are resilient, ethical, and secure. This is a pioneering role that combines traditional security engineering with the unique challenges of the AI lifecycle.
You will design security guardrails for Large Language Models (LLMs), protect training data integrity, and mitigate specific AI risks like model poisoning and prompt injection. Your mission is to enable the responsible adoption of AI across the enterprise while enhancing our own Cyber Defense operations with AI-driven automation.
What you will do:
AI/ML Security Architecture & Engineering
- Design and implement secure architectures for AI/ML solutions, ensuring model integrity and data protection across enterprise platforms.
- Embed security into the full model lifecycle, including training data ingestion, model deployment, inference, and continuous monitoring.
- Identify and mitigate AI-specific vulnerabilities such as prompt injection, model poisoning, evasion attacks, and data leakage.
AI-Driven Cyber Defense
- Integrate AI-driven detection and automation capabilities into Cyber Defense Operations and SOC environments.
- Develop AI/ML models to improve threat detection accuracy and automate incident response workflows.
- Collaborate with Digital and AI teams to establish secure AI design patterns and responsible AI adoption standards.
Governance & Guardrails
- Define and implement AI security guardrails, technical standards, and control frameworks for enterprise-wide use cases.
- Align AI security controls with emerging regulatory requirements, ethical AI principles, and risk obligations.
- Conduct security assessments on internal and third-party AI/ML models and their underlying infrastructure.
Tools & Technologies:
- AI/ML Frameworks: Familiarity with PyTorch, TensorFlow, Scikit-learn, or Hugging Face.
- LLM Security: Experience with tools like Giskard, Lakera, or OWASP Top 10 for LLMs.
- Cloud AI Services: AWS SageMaker, Azure AI/ML Studio, or Google Vertex AI.
- Security Tooling: Prompt injection filters, model monitoring tools, and adversarial robustness libraries (e.g., CleverHans, ART).
- Container Security: Docker and Kubernetes (K8s) for ML workloads.
What you bring:
- 6–8+ years in Cybersecurity, focused on securing AI/ML workloads or using Data Science for security.
- Technical Depth: Ability to analyze model architectures and data flows to identify security weaknesses.
- Adversarial Mindset: Understanding of how attackers exploit ML models and how to build defensive "guardrails."
- Collaboration Skills: Proven experience working with Data Scientists and AI Engineers to bridge the gap between "speed to market" and "secure by design."
Equal Opportunity Employer:
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