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Job Description
Structured overview of role & requirementsAbout This Role
Architect and implement secure, scalable CI/CD pipelines specially designed for AI/ML workloads including LLM-based and agentic AI systems.
Embed automated security controls (SAST, SCA, container/image scanning, policy-as-code) and AI-specific safeguards (data-poisoning, model integrity, prompt-injection) across AI pipelines and infrastructure.
Lead AI supply chain security, observability, incident response, and governance including provenance, attestation, model traceability, and adoption of secure AI frameworks (e.g., NIST AI RMF).
Minimum Requirements
8–10 years of DevSecOps experience including ML/AI SecOps and supply chain security.
Expertise in cloud security for Azure, AWS, or GCP, especially Kubernetes/Service Mesh and AI services.
Proficient in Python, Go, or similar programming languages plus advanced skills in Infrastructure-as-Code (Terraform) and GitOps.
Work Experience Required: 8–10 years in relevant DevSecOps roles as above.
Ideal Candidate Profile
Experienced senior technical SME at the intersection of AI/ML engineering, product development, platform infrastructure, and cybersecurity.
Strong background in cloud-native security architectures and implementing defense-in-depth for AI platforms.
Familiarity with provenance and attestation frameworks (e.g., SLSA, Sigstore) and managing AI/ML supply chains end-to-end.