Cyber -AI Cloud Security Engineer - Consultant

Deloitte•Jersey City, NJ

1d•$82,600 - $162,800•Onsite

About The Position

Deloitte is seeking an AI Cloud Security Engineer, Consultant to design, build, and secure next-generation AI-enabled products, platforms, and cloud environments. This engineering-focused role centers on artificial intelligence (AI) security architecture, AI Zero Trust, secure AI software development, cloud security engineering, and AI-enabled vulnerability discovery and remediation. The successful candidate combines hands-on software engineering with cybersecurity and cloud architecture experience to implement production-quality solutions and help clients build resilient AI platforms with security embedded throughout the technology lifecycle. At Deloitte, you will have opportunities to deepen your expertise across AI engineering, cloud security, secure software development, cybersecurity architecture, and emerging technology through technical learning, mentoring, communities of practice, and hands-on delivery.

Requirements

Bachelor’s degree in Information Technology, Computer Science, Artificial Intelligence, Cybersecurity, Computer Engineering, or Software Engineering; equivalent professional work experience in software engineering, AI engineering, cybersecurity, cloud security, application security, or technology consulting may be considered in lieu of a degree
2+ years of professional experience in software engineering, AI engineering, cybersecurity, cloud security, application security, or technology consulting, including delivery of production solutions through design, development, testing, deployment, and operational support
Hands-on experience with at least one of the following AI platforms: Anthropic Claude, Claude Code, OpenAI, Google Gemini, Amazon Bedrock, or Vertex AI
Experience designing or implementing cloud security architecture, secure application development, development, security, and operations (DevSecOps), infrastructure security, or AI security controls
Experience developing and deploying APIs, database-backed services, distributed systems, or cloud applications using Python, TypeScript, JavaScript, Go, or Java, including automated testing, deployment, and observability
Ability to travel up to 50%, on average, based on the work you do and the clients and industries/sectors you serve.
Limited immigration sponsorship may be available.

Nice To Haves

Experience securing Anthropic Claude, Claude Code, OpenAI, Google Gemini, Amazon Bedrock, or Vertex AI in production environments
Experience designing AI Zero Trust architectures or implementing security controls for models, agents, tools, data, and AI runtime environments
Experience conducting AI red teaming, adversarial testing, model evaluation, prompt security testing, RAG security testing, or responsible AI control testing
Experience building applications with React, Next.js, Node.js, FastAPI, GraphQL, Representational State Transfer (REST) APIs, microservices, or event-driven systems
Experience implementing policy-as-code, infrastructure-as-code security, or automated remediation with Terraform, Open Policy Agent, Checkov, tfsec, Kyverno, static application security testing (SAST), dynamic application security testing (DAST), software composition analysis, secrets scanning, or container security tools
Experience securing Kubernetes, containers, serverless platforms, cloud-native applications, or software supply chains, or delivering AI, cybersecurity, cloud, or application security solutions for financial services or healthcare organizations

Responsibilities

Designing secure architectures for generative AI, agentic AI, retrieval-augmented generation (RAG), model serving, and AI-enabled enterprise applications; applying AI Zero Trust principles across models, agents, tools, application programming interfaces (APIs), data, cloud workloads, development pipelines, and runtime environments
Embedding security throughout the AI software development lifecycle, including threat modeling, security gates, model and prompt versioning, evaluation datasets, approval workflows, release criteria, and AI-specific security testing; using AI coding tools with human review and secure engineering controls
Designing and implementing secure AI workloads across Amazon Web Services, Microsoft Azure, Google Cloud, and hybrid environments, including cloud landing zones, network isolation, private connectivity, logging, encryption, key management, data protection, containers, serverless workloads, Kubernetes, policy-as-code controls, and automated remediation
Building AI-assisted threat and vulnerability management capabilities that identify, analyze, prioritize, test, and remediate issues across code, applications, APIs, cloud configurations, infrastructure, containers, dependencies, and AI workloads; implementing human approval and validation before high-impact remediation is deployed
Developing production AI security applications and platforms using full-stack engineering, including user experiences, APIs, backend services, microservices, event-driven workflows, RAG, model integrations, observability, and unit, integration, end-to-end, performance, security, and AI evaluation testing
Collaborating with client engineering, security, product, data, risk, architecture, and executive stakeholders to lead discovery, technical workshops, architecture sessions, design reviews, solution delivery, production deployment, and operational handoff; producing reference architectures, patterns, standards, playbooks, and accelerators