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AI Security Senior Manager
Our Deloitte Cyber team understands the unique challenges and opportunities businesses face in cybersecurity. Join our team to deliver powerful solutions to help our clients navigate the ever-changing threat landscape. Through powerful solutions and managed services that simplify complexity, we enable our clients to operate with resilience, grow with confidence, and proactively manage to secure success.
As a Senior Manager, Strategy, Growth & Transformation specializing in Strategy in Deloitte Cyber's Cyber for AI practice, you will help clients solve complex AI cybersecurity, governance and risk strategy challenges as they scale generative AI and agentic systems across their client enterprises. This role sits at the intersection of executive client leadership and cross-capability strategic and solution delivery, combining deep AI cyber risk and governance expertise with the acumen to guide technical teams across Data, Infrastructure, Identity, Security Operations and Application Security. You will lead teams, shape strategic solutions during pursuits, develop reusable accelerators and drive high-quality outcomes for clients and teams alike.
Recruiting for this role ends on 12/31/2026.
Work you'll do
As a Senior Manager on the Cyber Strategy & Transformation team, you will be responsible for:
- Advise security, risk, data, technology, and business leaders on enterprise AI security strategy, governance, and risk appetite.
- Lead complex, multi-workstream AI security strategy projects and technology implementations from current-state assessment and target operating model design through governance implementation and adoption.
- Define scalable cybersecurity and governance patterns, including AI risk taxonomies, control frameworks, policy structures, intake and approval processes, and board or executive reporting models.
- Provide strategic oversight across Data, Infrastructure, Identity, Application Security, and Security Operations workstreams, ensuring technical delivery aligns with client risk appetite and strategic objectives.
- Lead pursuits and account growth by shaping differentiated strategic solutions, executive workshops, scope, estimates, pricing inputs, and delivery approaches.
- Oversee client relationships, engagement quality, delivery risk, and customer satisfaction across a portfolio of AI security strategy projects.
- Build practice capability through offerings, alliances, thought leadership, reusable accelerators, and development of future leaders.
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
Our Cyber Strategy & Transformation offering develops and transforms cyber programs in line with a client's strategic objectives, regulatory requirements, and risk appetite. It keeps the enterprise a step ahead of the evolving threat landscape and gives stakeholders confidence in the organization's cyber posture. Includes design of the cyber organization, governance, and risk assessments.
Qualifications
Required:
- Bachelor's degree in Computer Science, Engineering, Information Technology, Cybersecurity, Risk Management
- 8+ years of combined cybersecurity, risk management, or technology consulting experience
- Experience advising executives on AI/ML or emerging technology risk
- Experience leading enterprise AI governance, risk, or security strategy programs, including cross-functional delivery spanning identity, data, infrastructure, and application security domains
- Experience with AI/ML architectures and generative AI or agentic system risk in roles involving review of technical design and engagement with engineering leaders
- Experience applying AI risk and security frameworks or regulations, such as NIST AI RMF, ISO/IEC 42001, OWASP LLM Top 10, MITRE ATLAS, the EU AI Act, or sector-specific guidance
- Experience overseeing enterprise-scale, multi-workstream programs from strategic recommendations through technical implementation, including delivery risk management
- Experience leading client engagements, technical and strategic pursuits, executive relationships, delivery quality, and account growth
- Ability to travel 25-50%, on average, based on the work you do and the clients and industries/sectors you serve.
- Limited immigration sponsorship may be available.
Preferred:
- Experience designing enterprise AI cybersecurity controls, governance operating models, or AI risk reporting for executive or board stakeholders
- Experience with AI threat modeling, red teaming, or AI security evaluation programs
- Experience developing offerings, alliance relationships, or market-facing points of view in AI security strategy
- Experience with AI or ML development and operational lifecycles
- Certifications such as Certified Information Systems Security Professional (CISSP), Certified Information Security Manager (CISM), Certified Data Privacy Solutions Engineer (CDPSE), Artificial Intelligence Governance Professional (AIGP), or International Organization for Standardization/International Electrotechnical Commission 42001 Lead Implementer or Lead Auditor
- Experience working in a regulated industry or with regulators, boards, or audit committees on AI risk topics
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 $163,400 to $322,100.
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.
Salary Context
This $163K-$322K range is above the 75th percentile 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 in Demand for This Role
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. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($242K) sits 14% above the category median. Disclosed range: $163K to $322K.
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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