Principal DevSecOps Engineer - AI

A senior technical SME responsible for enabling secure, automated delivery of machine learning and AI workloads. This role operates at the intersection of AI/ML engineering, product development, platform infrastructure, and cybersecurity embedding security controls across the entire AI lifecycle, including CI/CD pipelines, model registries, vector databases, MCP services, and agent based AI orchestration. Key Responsibilities: Secure AI Platform Engineering Architect and implement secure, scalable, and resilient CI/CD pipelines purpose built for AI workloads, including LLM based and agentic AI systems. Automate secure build, deployment, and promotion workflows for AI workloads. ML/AI SecOps Integration Shift security left by embedding automated controls such as SAST, SCA, container/image scanning, and policy as code into pipelines. Implement AI specific security measures, including data poisoning safeguards, model integrity validation, and prompt injection mitigation. Infrastructure Platform Security Work with platform engineering teams to harden vector databases, retrieval APIs, and other AI-serving infrastructure. Establish secure Infrastructure as Code (IaC) templates, GitOps workflows, and micro segmented identity patterns using secrets management tools such as Vault. Ensure multi region, fault tolerant, and security hardened deployment architectures for AI platforms. Model Supply Chain Security Implement provenance, attestation, and traceability for datasets, training pipelines, and model artifacts. Protect the end to end AI supply chain from data ingestion through model deployment to prevent tampering, data integrity issues, or malicious model substitution. AI Observability Incident Response Collaborate with AI Platform and Security teams to develop monitoring and detection capabilities for AI specific threats, including adversarial examples, model extraction attempts, drift anomalies, and suspicious agent behaviors. Contribute to AI aware incident response procedures and threat hunting playbooks. Governance Compliance Champion the adoption of secure AI frameworks (e.g., NIST AI RMF) and define guardrails for responsible AI operations. Establish and maintain an AI Bill of Materials (AI BOM) to manage risk associated with third party models, components, and datasets. Required Qualifications: 8 10 yrs DevSecOps experience, ML/AI SecOps, supply chain security. Cloud Security expertise in securing Azure, AWS or GCP environment, particularly Kubernetes/Service Mesh, and their AI services Mandatory Experience: Python/Go, or similar programming language, along with advanced knowledge of IaC (Terraform) and GitOps patterns Preferred Skills: Experience with provenance/attestation frameworks (e.g., SLSA, Sigstore). Cloud security certifications (Azure, AWS, GCP). Hands on experience with KMS, secrets management platforms, and hardware rooted trust technologies. Disclaimer : This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.