Cyber Senior Consultant: AI Security, Infrastructure
About The Position
As an AI Infrastructure Senior Consultant on Deloitte's Cyber for AI team, you will lead technical workstreams that secure how AI agents, workloads, and users authenticate, receive permissions, and access enterprise tools and data. The role combines client advisory and hands-on engineering to translate requirements into architecture, integrations, and production controls. You will guide junior practitioners, lead demonstrations, and contribute to delivery estimates. The team helps organizations secure generative AI and agentic systems from design through production. Its infrastructure work includes secure AI landing zones, cloud and network architecture, container and model-serving platforms, policy automation, and observability. The role involves leading discovery and architecture analysis, designing and deploying secure AI environments, implementing infrastructure and platform controls, testing security and resilience, and resolving complex issues. The role is available in 50 U.S. locations. Recruiting for this position ends on 05/31/2027. The stated salary range is $105,400–$207,800, before applicable geographic differentials. Employees may also be eligible for a discretionary annual incentive program. Ability to travel 20%–50% on average is required.
Requirements
- 3–5+ years of combined cybersecurity and AI engineering experience, including hands-on production work in both domains.
- Experience designing, integrating, and securing production generative AI, RAG, or agentic workloads, including model, tool, and enterprise data connections.
- Hands-on experience designing and securing cloud, network, Kubernetes, container, and model-serving platforms in AWS, Azure, or Google Cloud, including secure AI landing zones.
- Experience implementing network segmentation, private connectivity, egress controls, DNS, TLS, firewalls, gateways, service meshes, and enterprise integration patterns.
- Experience implementing or reviewing infrastructure automation, Kubernetes configurations, deployment pipelines, and operational diagnostics using Terraform, AWS CloudFormation, or Azure Bicep.
- Experience with CI/CD pipelines, policy-as-code, Kubernetes RBAC, network policies, admission controls, and secrets management.
- Programming capabilities with Python, Go, Bash, PowerShell, HCL and/or YAML, and Git.
- Bachelor's degree in Computer Science, Engineering, Information Technology, Cybersecurity, or a related field.
- Ability to travel 20%–50% on average, based on assigned work and clients.
- Ability to work independently and collaboratively, communicate effectively in writing and verbally, and maintain professional relationships.
- Attention to detail and quality, ability to lead projects or workstreams, manage multiple priorities in a fast-paced environment, meet deadlines, and provide clear guidance to others.
Nice To Haves
- Experience in consulting, client delivery, or customer-facing engineering, including developing reusable technical assets, guiding junior engineers, or contributing to engineering standards.
- Experience with 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 using PyTorch, TensorFlow, or MLflow for machine learning development, deployment, or monitoring.
- Experience with AI platforms and model-serving technologies such as Amazon Bedrock/Bedrock AgentCore, SageMaker AI, Microsoft Foundry, Azure Machine Learning, Google Vertex AI, vLLM, KServe, NVIDIA Triton, NVIDIA Inference Microservices (NIM), or Ray Serve.
- Experience securing GPU infrastructure, model and container registries, software supply chains, signed artifacts, service meshes, or multi-tenant inference environments.
- Preferred certifications: CISSP, CCSP, CKA, CKS, AWS/Azure/GCP cloud certifications, Certified Terraform Associate, or Infrastructure Engineer Professional.
Responsibilities
- Own an infrastructure workstream, manage dependencies and milestones, and provide day-to-day guidance to consultants.
- Contribute technical demonstrations, prototypes, architecture, delivery effort, and estimation inputs to pursuits.
- Lead discovery and architecture analysis for managed AI services, self-hosted model stacks, cloud foundations, networks, and operational tooling.
- 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 management, artifact controls, infrastructure as code, 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.
Benefits
- Broad range of employee benefits (specific benefits are not detailed in the posting).
- Potential eligibility to participate in a discretionary annual incentive program.
- Professional development opportunities, including on-the-job learning, formal development programs, mentorship, and leadership opportunities.
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What This Job Offers
Job Type
Full-time
Career Level
Senior
