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About This Role
Discover your future at Citi
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Working at Citi is far more than just a job. A career with us means joining a team of approximately 219,000 dedicated people from around the globe. At Citi, you’ll have the opportunity to grow your career, give back to your community and make a real impact.
Job Overview
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Why Citi
Citi, the leading global bank, has approximately 200 million customer accounts and does business in more than 160 countries and jurisdictions. Citi provides consumers, corporations, governments, and institutions with a broad range of financial products and services, including consumer banking and credit, corporate and investment banking, securities brokerage, transaction services, and wealth management.
As a bank with a brain and a soul, Citi creates economic value that is systemically responsible and in our clients’ best interests. As a financial institution that touches every region of the world and every sector that shapes your daily life, our Enterprise Operations \& Technology teams are charged with a mission that rivals any large tech company. Our technology solutions are the foundations of everything we do from keeping the bank safe, managing global resources, and providing the technical tools our workers need to be successful to designing our digital architecture and ensuring our platforms provide a first\-class customer experience. We reimagine client and partner experiences to deliver excellence through secure, reliable, and efficient services.
Our commitment to diversity includes a workforce that represents the clients we serve from all walks of life, backgrounds, and origins. We foster an environment where the best people want to work. We value and demand respect for others, promote individuals based on merit, and ensure opportunities for personal development are widely available to all. Ideal candidates are innovators with well\-rounded backgrounds who bring their authentic selves to work and complement our culture of delivering results with pride. If you are a problem solver who seeks passion in your work, come join us. We’ll enable growth and progress together.
Why This Team
The Chief Information Security Office (CISO) is home to deeply talented colleagues that work to ensure the safety of Citi's clients', our revenue, our employees and our proprietary data. We manage information security as one end\-to end program – one with a clear mandate and accountability. Our mission is a program that is fully anchored to modern control and architectural frameworks, is fully aligned with the enterprise architecture of the firm and is deeply integrated into the businesses and functions.
About the Role
Citi's Offensive Security and Vulnerability Management (OSVM) team is hiring practitioners who can run security assessments against AI systems and use AI\-powered tools to make security testing faster and more effective.
The AI Offensive Security Analyst is an operational role on a team that runs penetration tests, vulnerability assessments, and Red Team adversarial evaluations across Citi's application portfolio. The AI dimension is twofold: you'll test AI systems as a target, and you'll use AI tools as part of how you work. We're looking for someone to help build the tools from scratch and who knows how to get the most out of them, knows their limits, and can operate in an environment where the right approach isn't always obvious yet.
What Makes This Different
The problems we're solving don't have playbooks yet.
- AI\-driven vulnerability management. When frontier models can generate thousands of findings in a weekend, the bottleneck shifts from discovery to triage, verification, and remediation. We're building the pipeline that makes this sustainable.
- Security architecture for the AI era. Defining how a global bank deploys, monitors, and governs agentic AI — from standards and evaluation frameworks to production runtime monitoring.
- AI at scale, not in a lab. Citi has deployed AI tools to 180,000\+ employees, equipped 30,000 developers with AI coding assistants, and is rolling out agentic AI capabilities firm wide. Securing that footprint and the platforms that underpin them is the job.
- Securing AI agents that can behave like insider threats. Frontier models can harvest credentials, escape sandboxes, and adapt when they detect monitoring. We're designing the containment architectures and runtime controls to operate them safely at enterprise scale.
- Real security engineering. This isn't a cyber seat where you'll spend your time in administration . You will be expected to understand the code, the architecture, the threats, and find solutions. You'll have the mandate and the backing to build something meaningful.
- Cyber Security Operations — Detection, triage, and response for a world where adversaries use AI to find and exploit vulnerabilities faster than traditional detection can keep up. Behavioral analytics for AI agents. Playbooks for AI\-originated attack scenarios.
What You'll Work On
- Testing AI applications and agents. GenAI applications, agentic systems, and LLM\-backed products require testing that goes beyond standard web app methodology. Prompt injection, indirect injection, retrieval poisoning, tool misuse, sensitive data extraction, unsafe delegation, these are real attack classes with real business impact. You'll design and execute test cases for them.
- Using AI to work better. AI\-assisted testing changes what's possible in a pen testing engagement: broader coverage, faster reconnaissance, automated validation of common vulnerability classes. You'll be expected to know how to use these capabilities effectively and to help the broader team adopt what works.
- Embedding security into development. Working with development teams and security engineers to make sure AI application assessments happen at the right points in the build process.
What We're Looking For
Security Testing Experience
- Practical experience running penetration tests or vulnerability assessments against web applications, APIs, or mobile applications. Alternatively, Red Team experience running adversary simulations as an operator
- Familiarity with common security testing tools: Burp Suite, nuclei, nmap, or similar
- Working knowledge of OWASP Top 10 and at least some exposure to OWASP LLM Top 10 or MITRE ATLAS
- Ability to triage findings and communicate risk clearly to technical and non\-technical audience
AI Literacy
- Hands\-on experience using LLM tools in a meaningful way, whether for security testing, security research, or your own workflows
- Enough understanding of how LLMs work (context windows, tool use, RAG, agentic chaining) to reason about where they can go wrong
- Familiarity with AI testing tools or frameworks, even from personal research or experimentation, is a real plus: Garak, PyRIT, PromptBench, Inspect AI, or equivalent
General
- Comfortable working on problems that don't have a playbook yet
- Can write clearly: test plans, findings reports, risk summaries
- Curious about the security implications of AI systems, not just the hype around them
Levels
- Assistant Vice President (C12 Mid \- Senior Level): 5\-7\+ years. Own workstreams end\-to\-end with real autonomy. You'll go deep on problems that most organizations don't even know they have yet.
- Vice President (C13 Senior \- Lead/Staff Level): 8\-10\+ years. Define technical approach, make architectural decisions, mentor others. The scope here is wider than most senior IC roles — you're not optimizing an existing system; you're designing ones that don't exist yet.
- Senior Vice President (C14 Lead/Staff \- Principal Level): 10\+ years. Set technical direction for a function and influence the firm's approach to AI security. If you've hit a ceiling elsewhere because the problem space isn't big enough, it's big enough here.
Why Citi, Why Now
- Real and urgent. Not an innovation lab. The threats are active, the work ships into production, and it protects one of the world's largest financial institutions.
- Technical teams. These are engineering\-led functions. Small teams, high autonomy, minimal governance overhead. We build tools, ship code, and measure ourselves by what we deliver — not slide decks.
- Strong mandate. Executive sponsorship to move fast. You'll have the backing and resources to act on what you find.
- Unique scope. Very few organizations operate at this intersection at this scale. The solutions you build will influence how the industry responds.
Education
- Bachelor's degree in Computer Science, Information Security, Engineering, or equivalent practical experience.
- Master’s degree preferred
*The role sits within Citi's Offensive Security and Vulnerability Management (OSVM) organization, a team whose mission is to safeguard Citi's clients, partners, and business sectors by integrating an attacker's mindset across the technology lifecycle.*
*We have multiple openings across various experience levels, from senior to principal, offering a dynamic environment for growth and impact. Location and compensation packages may be flexible and will be commensurate with experience and qualifications.*
*This job description provides a high\-level review of the types of work performed. Other job\-related duties may be assigned as required.*
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Job Family Group:
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Technology
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Job Family:
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Information Security
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Time Type:
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Full time
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Primary Location:
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Tampa Florida United States
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Primary Location Full Time Salary Range:
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$87,280\.00 \- $212,160\.00
In addition to salary, Citi’s offerings may also include, for eligible employees, discretionary and formulaic incentive and retention awards. Citi offers competitive employee benefits, including: medical, dental \& vision coverage; 401(k); life, accident, and disability insurance; and wellness programs. Citi also offers paid time off packages, including planned time off (vacation), unplanned time off (sick leave), and paid holidays. For additional information regarding Citi employee benefits, please visit citibenefits.com. Available offerings may vary by jurisdiction, job level, and date of hire.
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Most Relevant Skills
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Please see the requirements listed above.
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Other Relevant Skills
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Agentic Design, AI Penetration Testing, Artificial Intelligence (AI), Cybersecurity, Large Language Models (LLMs), MITRE ATT\&CK Framework, OWASP Top 10, Penetration Testing.
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Anticipated Posting Close Date:
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Aug 31, 2026
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Automated Processing and AI
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We use automated processing, including artificial intelligence, for our legitimate business interests (or our reasonable and appropriate business purposes) to identify and align the candidate's skills and abilities with a specific job opening. Additionally, if you so choose, or consent, we can match your skills and abilities to other suitable roles at Citi.
Importantly, all our hiring processes and decisions, including determining your suitability for a role, are conducted, checked, and decided by individuals. Our automated processing and AI do not involve relying on automatic or autonomous decision\-making. Please refer to any Jurisdictional Considerations, with specific provisions for your country (where relevant) for further details.
Illinois residents – AI Notice and Right
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*Citi is an equal opportunity employer, and qualified candidates will receive consideration without regard to their race, color, religion, sex, sexual orientation, gender identity, national origin, disability, status as a protected veteran, or any other characteristic protected by law.*
Salary Context
This $87K-$212K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Citi, 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($149K) sits 30% below the category median. Disclosed range: $87K to $212K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Citi AI Hiring
Citi has 32 open AI roles right now. They're hiring across AI Product Manager, AI/ML Engineer, AI Agent Developer, AI Software Engineer. Positions span New York, NY, US, Tampa, FL, US, Jacksonville, FL, US. Compensation range: $170K - $300K.
Location Context
AI roles in New York pay a median of $220,000 across 1,650 tracked positions.
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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 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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