Cyber Manager: AI Security, Infrastructure

Deloitte • Jersey City, NJ

5d • $134,500 - $265,100

About The Position

As a Cyber for AI: Manager, Infrastructure in Deloitte's Cyber for AI team, you will lead client engagements that secure the cloud, network, container, and model-serving foundations used for generative AI and agentic workloads. You will translate business and risk objectives into target architectures, participate in design and engineering decisions, and guide teams from assessment through implementation. You will also shape solutions during pursuits and help build repeatable delivery assets.

The Infrastructure capability focuses on secure AI landing zones, cloud and network architecture, container and model-serving platforms, policy automation, and observability. The team combines AI engineering and cybersecurity capabilities across identity, data, infrastructure, application security, and operations. It works with clients to integrate security controls into platform and delivery lifecycles so teams can move from experimentation to production while managing access, data, and infrastructure risks.

The role involves client-facing leadership, hands-on technical oversight, and practitioner coaching. It requires at least six years of combined cybersecurity and AI engineering experience, including production experience in both domains and experience architecting and leading production generative AI or agentic implementations. Travel is expected at an average of 20% to 50%, depending on the work and clients served. Recruiting for this role ends on 05/31/2027.

Requirements

• At least 6 years of combined cybersecurity and AI engineering experience, including hands-on production work in both domains.
• Experience architecting and leading production generative AI or agentic implementations, including technical decisions involving models, orchestration, identity, data, and infrastructure.
• Experience architecting and implementing secure AI landing zones in AWS, Azure, and/or Google Cloud, including network, Kubernetes, container, and model-serving platforms.
• Experience with security infrastructure such as private connectivity, segmentation, egress control, gateways, service meshes, secrets, encryption, and operational telemetry.
• Experience implementing infrastructure as code, CI/CD, and policy as code using Terraform, CloudFormation, Bicep, Open Policy Agent, Gatekeeper, Kyverno, or comparable tools.
• Experience with Kubernetes configuration, deployment pipelines, and operational diagnostics.
• Experience leading client-facing technology or cyber engagements, including scope, team management, delivery timelines, and quality management.
• Experience coordinating across AI Engineering, Identity, Data, Application Security, and DevSecOps teams.
• Bachelor's degree in Computer Science, Engineering, Information Technology, Cybersecurity, or a related field.
• Ability to travel 20% to 50% on average, based on the work and clients or industries served.
• Ability to work independently and collaboratively 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 lead projects or workstreams.
• Ability to manage and prioritize multiple tasks in a fast-paced, dynamic environment.
• Strong interpersonal skills and professional demeanor.
• Ability to meet deadlines.
• Ability to mentor and provide clear guidance to others.

Nice To Haves

• Experience with 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 building reusable technical assets, delivery methods, or automation for client engagements.
• 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

• Lead secure AI landing-zone assessments, architecture workshops, and implementation programs across cloud, network, Kubernetes, and model-serving platforms.
• Translate workload, risk, performance, and operational requirements into target architectures for managed and self-hosted AI environments.
• Provide hands-on technical oversight through design reviews, infrastructure-as-code and policy decisions, configuration and code reviews, and technical issue resolution.
• Manage engagement scope, teams, milestones, quality, and client expectations.
• Coordinate dependencies across AI engineering, identity, data, application security, DevSecOps, and operations teams.
• Shape solutions during pursuits through workshops, demonstrations, proofs of concept, estimates, and delivery plans.
• Develop reusable landing-zone blueprints, automation, and delivery methods.
• Coach practitioners on engineering quality.

Benefits

• Broad range of employee benefits.
• Potential eligibility to participate in a discretionary annual incentive program, subject to program rules and applicable factors.
• Professional development opportunities, including on-the-job learning, formal development programs, mentorship, skill building, and leadership opportunities.

What This Job Offers

• Job Type: Full-time
• Career Level: Manager