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Who we are looking for
We are looking for an experienced Vice President, AI Security Architecture reporting directly to the Managing Director, AI Security and Data Protection. You will serve as a senior technical leader responsible for defining enterprise security architecture strategy, patterns, standards, and guardrails for artificial intelligence, machine learning, and generative AI solutions. You will lead complex architecture decisions and partner with engineering, platform, product security, data, risk, and business leaders to enable AI capabilities that are secure by design, responsibly implemented, scalable, and aligned with internal control and regulatory expectations.
Why This Role is Important To Us
Global Cybersecurity protects State Street and its clients from the impact of cyber attacks by understanding cyber risk and mitigating it through a robust, continuously evolving cybersecurity program and control environment. Fusion & Security Operations provides real-time knowledge of current cyber threats to better prepare State Street for future threats. This senior Vice President role is critical to shaping the enterprise approach to AI security, influencing strategic technology decisions, and ensuring emerging AI capabilities are introduced with appropriate security, data protection, resilience, and governance controls. The role will establish scalable architecture practices that support innovation while protecting client trust and strengthening regulatory readiness.
What You Will Be Responsible For
In this role, you will:
- Provide senior technical leadership for the enterprise AI security architecture strategy, roadmap, and target-state design across artificial intelligence, machine learning, and generative AI capabilities.
- Lead the design and implementation of secure architecture patterns for AI systems, including data pipelines, model access, APIs, agents, retrieval-augmented generation, orchestration layers, and AI-enabled applications.
- Define, govern, and continuously mature enterprise AI security reference architectures, technical standards, guardrails, reusable patterns, architecture decision criteria, and exception processes.
- Serve as a senior authority for architecture and design reviews of complex or high-risk AI initiatives, ensuring security, privacy, resilience, and control requirements are incorporated early in the solution lifecycle.
- Lead AI-focused threat modeling across models, data flows, platforms, integrations, and user interactions, addressing risks such as data leakage, prompt injection, insecure model access, supply chain vulnerabilities, model misuse, and excessive agency.
- Translate emerging AI threats and regulatory expectations into practical security requirements, control designs, mitigation strategies, compensating controls, and risk-based architecture decisions.
- Partner with senior leaders across Engineering, Product Security, Platform, Cloud, Application, Data, Risk, Compliance, and Audit to align AI security priorities and resolve complex design or risk issues.
- Embed AI security controls into software development, DevSecOps, model development, deployment, validation, monitoring, and incident response workflows.
- Establish repeatable assurance and validation criteria for AI platforms, pipelines, models, third-party services, developer tools, and business-led AI use cases.
- Provide architecture oversight for generative AI tools, internal AI services, developer productivity solutions, and strategic enterprise AI programs.
- Advise governance forums and senior leadership on technical risk, architecture options, residual exposure, investment priorities, and recommended decisions.
- Support risk, audit, compliance, and incident management activities through clear documentation of architecture decisions, control designs, residual risks, remediation actions, and evidence.
- Mentor architects and security practitioners, build organizational capability, promote knowledge sharing, and continuously improve AI security architecture practices as technologies and threats evolve.
What We Value
These skills will help you succeed in this role:
- Extensive technical leadership experience across security architecture, secure design, application security, cloud security, product security, data protection, and modern engineering practices.
- Deep practical understanding of AI and machine learning architectures, generative AI, large language models, model and data pipelines, APIs, cloud-native platforms, and AI security risk patterns.
- Demonstrated ability to set architecture direction, lead complex cross-functional decisions, and translate emerging risks into scalable requirements, control designs, implementation patterns, and executive recommendations.
- Significant experience leading architecture reviews, threat modeling, technical risk assessments, and secure-by-design programs in large, complex, regulated enterprises.
- Strong executive communication, collaboration, and influencing skills across Security, Engineering, Technology, Data, Risk, Compliance, Audit, and business leadership.
- Proven ability to mentor senior practitioners, establish architecture governance, and drive adoption of enterprise standards across global teams.
- Execution-focused leadership, sound judgment, strong problem-solving skills, and a commitment to measurable risk reduction and sustainable control outcomes.
- Curiosity, adaptability, and continuous learning in a rapidly evolving AI security landscape.
Education & Preferred Qualifications
- Degree required: Bachelor’s degree in Computer Science, Information Security, Engineering, or a related discipline; advanced degree preferred.
- Years of experience: 10+ years of progressive experience in cybersecurity, including significant experience in security architecture, application security, product security, cloud security, or security engineering.
- Certifications required or preferred: CISSP, CCSP, CSSLP, cloud security, architecture, or comparable industry certifications preferred.
- Knowledge of tools or technologies: Cloud-native architectures; AI and machine learning platforms; generative AI and large language model solutions; APIs; data pipelines; DevSecOps and CI/CD; threat modeling; security testing; identity and access management; data protection; logging, monitoring, and security automation.
- Additional language requirements: N/A.
- Other qualifications: Experience securing enterprise-scale platforms and applications, working in regulated environments, establishing reusable architecture standards, and communicating technical risk to engineering and senior leadership audiences.
Additional Requirements
Additional requirements for this role include the ability to coordinate across global teams and time zones, support high-priority architecture, risk, audit, and incident activities as needed, and manage sensitive information in accordance with company requirements. Travel may be required based on business needs.
Work Requirement
- Hybrid Work Requirement: Hybrid, subject to company and location-specific requirements.
- Shift Timings: Standard business hours with flexibility to support global stakeholders and critical security, architecture, or regulatory needs.
Salary Range:
$120,000 - $217,500 Annual The range quoted above applies to the role in the primary location specified. If the candidate would ultimately work outside of the primary location above, the applicable range could differ.
Employees are eligible to participate in State Street’s comprehensive benefits program, which includes: our retirement savings plan (401K) with company match; insurance coverage including basic life, medical, dental, vision, long-term disability, and other optional additional coverages; paid-time off including vacation, sick leave, short term disability, and family care responsibilities; access to our Employee Assistance Program; incentive compensation including eligibility for annual performance-based awards (excluding certain sales roles subject to sales incentive plans); and, eligibility for certain tax advantaged savings plans.
For a full overview, visit https://hrportal.ehr.com/statestreet/Home .
About State Street
Across the globe, institutional investors rely on us to help them manage risk, respond to challenges, and drive performance and profitability. We keep our clients at the heart of everything we do, and smart, engaged employees are essential to our continued success.
We are committed to fostering an environment where every employee feels valued and empowered to reach their full potential. As an essential partner in our shared success, you’ll benefit from inclusive development opportunities, flexible work-life support, paid volunteer days, and vibrant employee networks that keep you connected to what matters most. Join us in shaping the future.
As an Equal Opportunity Employer, we consider all qualified applicants for all positions without regard to race, creed, color, religion, national origin, ancestry, ethnicity, age, disability, genetic information, sex, sexual orientation, gender identity or expression, citizenship, marital status, domestic partnership or civil union status, familial status, military and veteran status, and other characteristics protected by applicable law.
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Salary Context
This $120K-$217K range is below the median for AI/ML Engineer roles in our dataset (median: $176K across 1540 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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At State Street, 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 $212,000 based on 11,857 positions with disclosed compensation. This role's midpoint ($168K) sits 20% below the category median. Disclosed range: $120K to $217K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
State Street AI Hiring
State Street has 17 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Austin, TX, Boston, MA, US, Princeton, NJ. Compensation range: $157K - $217K.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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