Cyber Senior Manager: AI Security, Infrastructure
About The Position
As a Cyber for AI Infrastructure Senior Manager on Deloitte's Cyber for AI team, you will help clients address cloud and infrastructure security challenges across managed and self-hosted artificial intelligence platforms. The role combines client leadership with hands-on delivery, applying infrastructure security and AI engineering experience to architect, build, and deploy production-grade solutions. You will lead teams, shape technical solutions during pursuits, develop reusable accelerators, and deliver outcomes for clients and teams. The Cyber for AI team helps organizations secure generative AI and agentic systems from design through production, bringing together AI engineering and cybersecurity skills across identity, data, infrastructure, application security, and operations. The Infrastructure capability focuses on secure AI landing zones, cloud and network architecture, container and model-serving platforms, policy automation, and observability. The position is available in multiple listed U.S. locations and recruiting for the role ends on May 31, 2027.
Requirements
- 8 or more years of combined cybersecurity and AI engineering experience, including hands-on production work in both domains and experience shaping and delivering production generative AI or agentic programs with managed or self-hosted architectures and enterprise control integration.
- Experience designing and securing AI landing zones and infrastructure across AWS, Microsoft Azure, or Google Cloud, including cloud and network architecture, Kubernetes, container, GPU, and model-serving platforms.
- Experience with security infrastructure including private connectivity, segmentation, egress control, service-to-service security, tenant isolation, secrets management, encryption, resilience, and observability.
- Experience implementing infrastructure as code, CI/CD, and policy as code using tools such as Terraform, CloudFormation, Bicep, Open Policy Agent, Gatekeeper, or Kyverno, or comparable tools.
- Experience reviewing architecture, automation, platform configuration, and critical prototypes.
- Ability to guide teams through infrastructure decisions.
- Experience leading consulting client engagements, technical pursuits, executive relationships, delivery quality, and account growth.
- Bachelor's degree in Computer Science, Engineering, Information Technology, Cybersecurity, or a related field.
- Ability to travel 25% to 50% on average, based on the work and clients and industries served.
- Ability to work independently and collaborate as part of a team.
- Effective written and verbal communication skills.
- Meticulous attention to detail and quality of work product.
- Ability to build and sustain professional relationships.
- Ability to manage and prioritize multiple tasks in a fast-paced and dynamic environment.
- Strong interpersonal skills and professional demeanor.
- Ability to meet deadlines.
Nice To Haves
- Experience with managed AI services and self-hosted or open-weight AI platforms.
- Experience in AI threat modeling, red teaming, security evaluations, secure AI development practices, or AI-specific monitoring.
- Experience building offerings, reusable technical assets, alliance relationships, or market-facing points of view.
- Experience with machine learning tools and frameworks such as PyTorch, TensorFlow, or MLflow.
- 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.
- CISSP certification.
- CCSP certification.
- Certified Kubernetes Administrator (CKA) or Certified Kubernetes Security Specialist (CKS) certification.
- AWS, Azure, or GCP cloud certifications.
- Certified Terraform Associate or Infrastructure Engineer Professional certification.
Responsibilities
- Advise security, infrastructure, technology, and AI leaders on secure platform strategy for managed and self-hosted AI workloads.
- Lead multi-workstream infrastructure programs from current-state assessment and target architecture through implementation and operational adoption.
- Define scalable control patterns for network isolation, Kubernetes, model serving, GPU environments, infrastructure as code, policy enforcement, software supply chain, and observability.
- Provide hands-on technical oversight through architecture reviews, critical prototypes, platform decisions, and resolution of complex delivery issues.
- Lead pursuits and account growth by shaping differentiated solutions, demonstrations, scope, estimates, pricing inputs, and delivery approaches.
- Oversee client relationships, engagement quality, delivery risk, and customer satisfaction.
- Build practice capability through offerings, alliances, reusable accelerators, technical guidance, and development of future leaders.
- Lead projects or workstreams, mentor others, and provide clear guidance.
Benefits
- Broad range of employee benefits.
- May be eligible 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
