Work you'll do
As an AI Security Analyst on the Infrastructure team, you will be responsible for
- Build secure AI landing-zone components across cloud accounts or subscriptions, networks, identity, compute, Kubernetes, model services and observability.
- Design and deploy secure AI landing zones and model-serving environments across AWS, Azure, Google Cloud or Kubernetes-based platforms.
- Lead implementation of network isolation, private access, egress controls, cluster security, secrets, artifact controls, IaC and policy enforcement.
- Engineer and review Kubernetes and model-serving patterns for tenant isolation, GPU workloads, autoscaling, resilience and controlled enterprise integration.
- Plan and execute platform security, resilience and operational testing, diagnose complex failures, and drive issues through resolution.
- Own an infrastructure workstream, manage dependencies and milestones, and provide day-to-day guidance to consultants.
- Lead technical demonstrations and prototypes, and contribute architecture, effort and estimation inputs to pursuits.
A successful candidate would possess these skills:
- Ability to independently lead a technical workstream and manage priorities, dependencies and delivery commitments.
- Clear communication of architecture decisions, technical tradeoffs, risks and implementation status to engineering and business stakeholders.
- Ability to guide consultants, review technical work and create a collaborative delivery environment.
- Strong demonstration and technical storytelling skills for workshops, prototypes and client readouts.
Qualifications
Required:
- Bachelor's degree in Computer Science, Engineering, Information Technology, Cybersecurity or a related field; alternatively, equivalent demonstrated experience.
- 2+ years of combined cybersecurity and AI engineering experience, including hands-on production work in both domains.
- Hands-on experience designing, integrating and securing production generative AI, RAG or agentic workloads, including model, tool and enterprise data connections.
- Hands-on depth in AWS, Azure or Google Cloud, with working knowledge of infrastructure security patterns in the other major cloud platforms.
- Strong hands-on experience designing and securing cloud and network infrastructure, Kubernetes or container platforms, and production AI or model-serving workloads.
- Strong knowledge of network segmentation, private connectivity, egress controls, DNS, TLS, firewalls, gateways, service meshes and enterprise integration patterns.
- Implementation experience with Terraform or comparable IaC, CI/CD pipelines, policy-as-code, Kubernetes RBAC, network policies, admission controls and secrets.
- Ability to design, build and review infrastructure automation using Python, Go, Bash, PowerShell or a comparable language, together with HCL or YAML, Git and CI/CD tooling.
- Ability to travel 25% to 50%, on average, based on the work you do and the clients and engagements you serve.
Preferred:
- Previous consulting, client delivery or customer-facing engineering experience.
- Experience across both managed AI services and self-hosted or open-weight AI platforms.
- Experience with AI threat modeling, red teaming, security evaluations, secure AI development practices or AI-specific monitoring.
- Experience developing reusable technical assets, guiding junior engineers or contributing to engineering standards.
- Familiarity with the broader machine learning lifecycle and frameworks such as PyTorch, TensorFlow or MLflow.
- Experience with AI platforms and model-serving technologies such as Amazon Bedrock, Bedrock AgentCore or SageMaker AI; Microsoft Foundry or Azure Machine Learning; Google Vertex AI; vLLM; KServe; NVIDIA Triton or NIM; Ray Serve; or comparable platforms.
- Experience securing GPU infrastructure, model and container registries, software supply chains, signed artifacts, service meshes or multi-tenant inference environments.
- Relevant certifications such as CISSP, CCSP, CKA, CKS, or cloud and Terraform certifications