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About This Role
Deloitte is seeking an AI Cloud Security Engineer, Manager to lead the design, implementation, and security of artificial intelligence (AI)-enabled products, platforms, and cloud environments. This engineering-focused Manager role combines AI security architecture, AI Zero Trust, secure AI software development, cloud security engineering, and AI-enabled vulnerability discovery and remediation.
You will lead technical delivery for complex client programs, apply hands-on software engineering, cybersecurity, and cloud architecture expertise, and guide teams in building resilient AI platforms with security embedded throughout the technology lifecycle.
Recruiting for this role ends on 12/31/2026.
Work you'll do
As an AI Cloud Security Engineer, Manager on the Cyber and Cloud Security team, you will be responsible for:
- Leading the design of secure architectures for generative AI, agentic AI, retrieval-augmented generation (RAG), model serving, and AI-enabled enterprise applications; defining security boundaries and AI Zero Trust controls across models, agents, tools, application programming interfaces (APIs), data, cloud workloads, development pipelines, and runtime environments
- Establishing and implementing security practices across the AI software development lifecycle, including threat modeling, security gates, model and prompt versioning, evaluation datasets, approval workflows, release criteria, and testing for prompt injection, data leakage, unsafe tool use, model abuse, and policy violations
- Leading the design and implementation of secure AI workloads across Amazon Web Services (AWS), 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, and infrastructure-as-code controls
- Directing AI-assisted vulnerability and threat management capabilities that identify, analyze, prioritize, test, and remediate issues across code, applications, APIs, cloud configurations, infrastructure, dependencies, containers, and AI workloads; establishing human approval and validation before high-impact remediation is deployed
- Designing and building 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 automated testing
- Leading client discovery, architecture sessions, technical workshops, design reviews, program delivery, production deployment, and operational handoff; managing technical workstreams, mentoring engineers, and creating reusable reference architectures, standards, playbooks, and accelerators
A successful candidate would possess these skills:
- 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 lead projects or workstreams
- Ability to manage and prioritize multiple tasks in a fast-paced and dynamic environment
- Strong interpersonal skills and professional demeanor
- Ability to meet deadlines
- Ability to mentor and provide clear guidance to others
The team
Deloitte's Cyber and Cloud Security teams help organizations adopt emerging technologies securely and at scale. Our professionals work across security architecture, cloud engineering, application security, AI, risk, and technology transformation to help clients design resilient platforms and embed security into how technology is built and operated.
This future-focused engineering capability operates at the intersection of AI, cybersecurity, cloud, and software delivery. The team builds reusable platforms, accelerators, reference architectures, and implementation patterns that help clients secure AI innovation from design through production.
Qualifications
Required:
- 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
- 6+ years of professional experience in software engineering, AI engineering, cybersecurity, cloud security, application security, or technology consulting, including leadership of AI, software engineering, cloud security, cybersecurity, or technology transformation programs
- 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.
Preferred:
- 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 developing applications using 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 using 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
The wage range for this role takes into account the wide range of factors that are considered in making compensation decisions including but not limited to skill sets; experience and training; licensure and certifications; and other business and organizational needs. The disclosed range estimate has not been adjusted for the applicable geographic differential associated with the location at which the position may be filled. At Deloitte, it is not typical for an individual to be hired at or near the top of the range for their role and compensation decisions are dependent on the facts and circumstances of each case. A reasonable estimate of the current range is $134,500 to $265,100.
You may also be eligible to participate in a discretionary annual incentive program, subject to the rules governing the program, whereby an award, if any, depends on various factors, including, without limitation, individual and organizational performance.
#CyberES27
Salary Context
This $134K-$265K range is above the median for AI/ML Engineer roles in our dataset (median: $178K across 2239 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Deloitte, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $212,040 based on 13,780 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($199K) sits 6% below the category median. Disclosed range: $134K to $265K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Deloitte AI Hiring
Deloitte has 128 open AI roles right now. They're hiring across AI/ML Engineer, Data Engineer, AI Safety, AI Product Manager. Positions span Jacksonville, FL, Morristown, NJ, US, New York, NY. Compensation range: $118K - $338K.
Location Context
Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 roles, $210,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (140) are outnumbered by mid-level (5,630) and senior (4,176) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $266,050 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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