AI Security Engineer

Job Title: AI Security Engineer Role Overview We are seeking a skilled AI Security Engineer to help design, implement, and maintain secure cloud-native AI platforms and applications. This role focuses on securing AI/ML workloads, cloud infrastructure, APIs, data pipelines, and modern software supply chains across enterprise environments. The ideal candidate will have a strong background in cloud security engineering , combined with practical exposure to AI/ML technologies, AI security risks, and MLSecOps practices . You will work closely with cloud, engineering, DevOps, and AI/ML teams to implement scalable security controls and support secure adoption of AI-enabled solutions. This role also includes supporting software and AI supply chain security initiatives through management and validation of SBOM, CBOM, AIBOM, and KBOM artifacts. Key Responsibilities 1. Cloud Security Engineering Design, implement, and maintain security controls for cloud-native environments and AI/ML workloads. Secure cloud infrastructure, services, APIs, containers, and workloads across public cloud platforms. Experience in managing CSPM tools such as Prisma, wiz, orca etc. Implement and manage: IAM and least-privilege access controls Network segmentation and secure connectivity Encryption and key management Secrets management and workload isolation Logging, monitoring, and alerting controls Conduct cloud security assessments, configuration reviews, and risk analysis. Support security hardening for cloud-hosted AI services and model-serving infrastructure. 2. AI/ML Security Support secure deployment and operation of AI/ML systems, including: LLM-based applications RAG systems Model APIs and inference services Agentic AI workflows Identify and assess AI-specific security risks such as: Prompt injection and jailbreak attacks Model abuse and unauthorized access Data poisoning and sensitive data leakage Model inversion and extraction attacks Implement AI security controls including: Prompt filtering and validation Output sanitization Access restrictions and guardrails Data protection and context isolation Participate in AI threat modeling and security design reviews. 3. MLSecOps / DevSecOps Integrate security controls into AI/ML and cloud CI/CD pipelines. Support secure practices for: Model training and deployment Container security Infrastructure as Code (IaC) Dependency and artifact validation Implement automated security checks for: Models and datasets APIs and infrastructure Containers and cloud workloads Assist with secure model versioning, rollback, and deployment validation. 4. Software & AI Supply Chain Security Support secure software and AI supply chain initiatives. Generate, validate, and manage: SBOM (Software Bill of Materials) CBOM (Cryptography Bill of Materials) AIBOM (AI Bill of Materials) KBOM (Knowledge Bill of Materials) Integrate BOM generation and validation into CI/CD and deployment workflows. Track dependencies, model provenance, datasets, third-party AI integrations, and cryptographic components. Support vulnerability management and compliance activities related to software and AI supply chains. Required Qualifications Bachelor’s degree in Computer Science, Cybersecurity, Information Security, or related field (or equivalent practical experience). 4–7 years of experience in: Cloud security engineering Security operations or security engineering Application or infrastructure security Hands-on experience with cloud-native security controls, architectures and CSPM tools. Understanding of: IAM, encryption, network security, and secrets management Secure SDLC and vulnerability management Containers, APIs, and CI/CD security Familiarity with AI/ML concepts and AI security risks. Experience with scripting/programming languages such as: Python (preferred) Bash, Go, or JavaScript/TypeScript Preferred Qualifications Experience with: AI/ML platforms and orchestration frameworks RAG systems, vector databases, and model-serving platforms Infrastructure as Code (Terraform, CloudFormation, etc.) Security automation and cloud compliance tooling Familiarity with: OWASP Top 10 for LLMs NIST AI RMF MITRE ATLAS MLSecOps and MLOps concepts Experience working with: BOM standards and tooling (CycloneDX, SPDX, etc.) Container and artifact security solutions Secure software supply chain practices Relevant cloud or security certifications are a plus. Core Competencies Strong analytical and troubleshooting skills Ability to identify and mitigate cloud and AI security risks Effective communication and collaboration across technical teams Strong ownership mindset and attention to detail Ability to work in fast-paced, engineering-driven environments What Makes This Role Unique This role combines cloud security engineering with modern AI/ML security practices . You will help secure cloud-native AI systems, protect AI-enabled workloads, and strengthen software and AI supply chain security through practical implementation of controls, automation, and secure engineering practices.