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We are seeking a Lead AI Security Engineer to join the AI Foundation team and drive the security strategy and implementation for Enterprise AI and Scientific AI workloads across Azure and GCP platforms.
This is a technical leadership role where you will shape and evolve the AI Security Framework, translating controls into working platform configurations and establishing engineering standards for security as code through Terraform, GitOps CI/CD security gates, and policy-as-code that continuously attests to compliance. You will lead a team of AI Security Engineers, partner with Lead AI Security auditors and AI Engineering leads in a high-talent-density domain, and own the end-to-end security posture of AI platforms, ensuring implementations consistently exceed governance requirements.
- Define and own the technical direction for translating AI Security control descriptions into working platform configurations and defensible implementations
- Establish engineering standards and reusable patterns for security controls as code using Terraform, GitOps CI/CD pipelines, and policy-as-code frameworks
- Design evidence-generation frameworks that produce machine-verifiable proof of adherence to control descriptions at scale
- Architect Kubernetes GPU node pool segmentation, VNet/VPC isolation, and private endpoints across Azure and GCP
- Define authorization models for RBAC/ABAC least-privilege access, managed/workload identity, and risk-based access controls
- Lead the design of a hardened API integration layer and centralized gateway with tool-level access controls
- Set the strategy for LLM guardrails covering prompt/response injection, content filtering, kill-switches, and inference rate limits
- Architect observability pipelines for anomaly and drift detection, restricted-access logs, and end-to-end traceability
- Govern training-data and model isolation strategies to support robustness and prevent model theft
- Mentor engineers and lead cross-functional collaboration with Lead AI Security auditors and AI Engineers to ensure implementations pass governance consistently
- Drive classification and compliance programs aligned with ISO 27001, EU AI Act, and GDPR requirements
- Represent the AI Security Engineering practice in architecture reviews, roadmap planning, and stakeholder communications
- 5+ years of experience in SDLC foundations and cloud platform engineering with an AI/ML focus, including demonstrable experience leading technical teams or initiatives
- Security-first mindset with the proven ability to set direction and translate control intent into defensible implementations, mentoring engineers and partnering with lead specialists
- Deep hands-on and architectural experience with Azure, GCP, and Kubernetes (AKS/GKE) including GPU node pools and VNet/VPC segmentation
- Expert-level command of Terraform, GitOps workflows, and policy-as-code within CI/CD pipelines (Azure DevOps/GitHub Actions), with experience defining standards and reusable modules
- Advanced knowledge of IAM concepts including RBAC/ABAC least-privilege, managed/workload identity, and geo-aware risk-based access controls
- Proven track record designing hardened API integration layers, centralized MCP gateways, and LLM guardrails for prompt/response injection and content filtering
- Expertise in architecting observability stacks using OpenTelemetry, Prometheus, Loki, Tempo, and Grafana for anomaly and drift detection
- Strong grasp of data classification, training-data/model isolation, and model-theft prevention practices
- Solid background in compliance frameworks including ISO 27001, EU AI Act, and GDPR, with experience translating regulatory requirements into technical roadmaps
- Excellent stakeholder communication, mentoring, and technical leadership skills
- Excellent command of written and spoken English (B2+ level)