Cyber- DT&P- Infrastructure - AI Security Analyst

AI Security Analyst, Infrastructure
As an AI Security Analyst specializing in Infrastructure in Deloitte Cyber's Cyber for AI practice, you will own technical workstreams that secure the cloud, network, container and model-serving foundations used to run generative AI and agentic workloads. You will combine client advisory with hands-on engineering, turning requirements into architectures, integrations and production controls. You will guide junior practitioners, lead demonstrations and help estimate delivery work.
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.
The team
Deloitte Cyber's Cyber for AI practice helps organizations secure generative AI and agentic systems from design through production. The team brings together AI engineering and cyber skills across identity, data, infrastructure, application security and operations.
We work with clients to make security controls part of the platform and delivery lifecycle, so teams can move from experiments to production without creating unmanaged access, data or infrastructure risk.
Within the practice, the Infrastructure capability focuses on secure AI landing zones, cloud and network architecture, container and model-serving platforms, policy automation and observability.
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
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