What are the responsibilities and job description for the Principal, Application Data & Cloud Security (AI) position at Cargill?
Job Purpose and Impact
Key Accountabilities
- The Principal AI Security Architect will help enable secure, responsible, and scalable adoption of Artificial Intelligence across the enterprise. This role will lead the definition of security architecture, standards, and guardrails for AI platforms—including Generative AI and Agentic AI—ensuring Cargill’s data, models, and digital assets remain protected as AI innovation accelerates.
Key Accountabilities
- Define and evolve enterprise AI security architecture, standards, and control frameworks.
- Design reusable secure reference architectures and guardrails for AI workloads across cloud and platform environments.
- Partner with engineering, data science, and architecture teams to integrate automated, scalable AI security controls across the AI lifecycle.
- Lead security strategy for Generative AI and Agentic AI, including LLM protection, prompt safety, agent orchestration, tool execution, and human‑in‑the‑loop controls.
- Identify and mitigate emerging AI threats such as prompt injection, model misuse, data poisoning, inference abuse, and AI supply‑chain risks.
- Assess and document the security impact of new AI systems, agents, and integrations on the broader enterprise environment.
- Guide evaluation and adoption of AI security technologies to ensure solutions are effective and cost‑efficient.
- Provide technical leadership and mentorship to security engineers and influence AI security roadmaps.
- Stay current on AI, cloud, and security trends and translate them into actionable security strategies.
- Minimum requirement of 6 years of relevant work experience. Typically reflects 10 years or more of relevant experience.
- 10 years of experience in cybersecurity architecture or engineering roles.
- Strong understanding of modern AI/ML architectures, pipelines, and platforms.
- Hands-on experience with Generative AI and Agentic AI security, including LLMs, AI agents, prompt safety testing, and AI governance.
- Ability to clearly communicate AI security risks to technical and non-technical stakeholders.
- Strong analytical, problem-solving, and decision-making skills.
- Demonstrated adaptability in fast-changing environments with the ability to manage multiple priorities.