2 months ago
Bengaluru, IndiaSenior
Responsibilities
- Develop and maintain security and resilience for enterprise-grade AI and Generative AI services.
- Secure LLMs, agentic AI systems, multi-agent workflows, inference services, fine-tuning pipelines, and runtime execution environments.
- Implement security controls for Model Context Protocol and related agent integration patterns.
- Manage AI platform security across public cloud AI services, self-hosted models, and third-party AI providers.
- Build automated Python and policy-as-code security guardrails for access control, data protection, model usage limits, and runtime monitoring.
- Partner with AI Platform, Cloud Platform, DevOps, Software Engineering, and Compliance teams to embed security into the AI development lifecycle.
- Perform threat modeling and risk assessments for AI architectures, including RAG pipelines, vector databases, agent frameworks, and model supply chains.
- Support investigation, containment, remediation, and lessons learned for AI-related security incidents.
- Contribute to enterprise AI security standards, reference architectures, and best practices.
- Mentor junior engineers and serve as a senior technical voice across engineering and leadership teams.
Requirements
- Bachelor’s or Master’s degree in Computer Science, Cybersecurity, Artificial Intelligence/Machine Learning, or a related field.
- At least 6 years of cybersecurity engineering experience with ownership of complex, enterprise-scale systems.
- Demonstrated experience addressing security challenges involving LLMs, Generative AI, or agentic AI systems, including model misuse, prompt injection, hallucination risk, and data governance.
- Strong understanding of cloud security across AWS, Azure, and/or GCP, including IAM, networking, encryption, logging, and monitoring.
- Hands-on Python experience and security automation for scalable AI security control enforcement.
- Experience securing containerized and distributed systems, including Kubernetes and service meshes.
- Familiarity with model registries, vector databases, RAG pipelines, fine-tuning workflows, and inference gateways.
Categories
About Cargill
Cargill builds and operates global food and agriculture supply chains, selling commodities, food ingredients, animal nutrition, and risk-management services to food manufacturers, retailers, and farmers. It trades and processes grains, oilseeds, cocoa, and proteins, and provides logistics and biofuel solutions. Founded in 1865 and headquartered in Wayzata, Minnesota, Cargill is privately held and family-owned.