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Job Overview
The Technology and Cyber Compliance and Operational Risk Office (TCCORO) at Citi is the firm’s reliable second set of eyes. Our mission is to drive comprehensive and consistent practices designed to identify, measure, monitor, report and manage operational and compliance risks while promoting the implementation of actions to address root causes which may lead to unintended operational losses or regulatory breaches. TCCORO provides the specialist subject matter experts to challenge Enterprise, Infrastructure, Operations and Technology entities across the firm. We are the technology and cyber conscience of the bank. In line with the Operational Risk Management (ORM) and Independent Compliance Risk Management (ICRM) frameworks, we aim to ensure that the internal controls that are designed to mitigate technology, cyber and AI risks are managed, mitigated, and aligned with our risk appetite.
The Tech 2LOD Risk Sr Officer is part of the Finance, Risk and End User Computing Second Line of Defense (2LOD) within TCCORO and provides independent oversight of technology, cyber and AI risk across applications, agentic workflows, digital assistants, end user computing tools, associated processes and control environments The role will leverage technology subject matter expertise, business experience, data analysis techniques, current events, and industry trends and best practices to inform the prioritization of risks and the second-line’s approach for associated challenge and influence activities. This position actively works with our ORM and Compliance partners and other stakeholders to provide subject matter expertise in line with our operational and compliance risk management frameworks. A successful candidate will have expertise in technology, cyber and AI risk in global financial services and be able to demonstrate a comprehensive understanding of the subject matter and how it applies to related risks. They should have a strong track record in technology, cyber and AI risk management and/or a strong technical background with excellent analytical skills. A successful candidate should also demonstrate a strong interest in the field and a passion for risk management.
Responsibilities:
- Assesses technology, cyber, data, and AI risks associated with new initiatives, strategic programs, digital transformation efforts, emerging technologies, cloud implementations, and third-party solutions, providing credible challenge to ensure risks are appropriately identified, assessed, and managed.
- Reviews and evaluates compliance, technology, cybersecurity, data, and AI governance policies, standards, procedures, technologies, tools, and governance processes to assess their design and effectiveness and ensure alignment with regulatory requirements and the firm's risk appetite.
- Leads and partners with stakeholders across Technology, Cyber, Compliance, and Risk Management to conduct risk-based monitoring reviews, independent assessments, control evaluations, and sample-based testing, including the development of workpapers, observations, issues, and executive reporting.
- Challenges the design, adequacy, and effectiveness of technology, cyber, data, and AI controls, recommending actions to address control weaknesses, emerging risks, and the root causes of operational risk events.
- Evaluates Artificial Intelligence (AI), Generative AI (GenAI), Machine Learning (ML), Agentic AI, AI Agents, Digital Assistants, AI-enabled software development, large language models (LLMs), third-party AI services, and emerging AI technologies to assess governance, security, privacy, data management, resiliency, model risk, regulatory compliance, and responsible AI practices.
- Provides independent oversight and challenge of AI agent development, deployment, monitoring, and decision-making frameworks, including autonomous and semi-autonomous workflows, human-in-the-loop controls, agent orchestration, prompt management, and accountability mechanisms.
- Knowledge of emerging technology, cybersecurity, and AI regulatory requirements, industry standards, and supervisory expectations, including technology resilience, cloud computing, cyber defense, secure software development, identity and access management, AI governance, and responsible AI practices.
- Reviews risks associated with program and project delivery and challenges implementation readiness, governance, resiliency, and control effectiveness for technology, cyber, and AI-enabled solutions.
- Monitors and challenges Key Risks, Key Risk Indicators (KRIs), and emerging risk trends, including those related to AI adoption, agent performance, automation, and technology resiliency, and drives timely remediation of identified issues.
- Executes deep-dive reviews, thematic assessments, audit and regulatory support activities, and executive communications, while ensuring compliance with applicable laws, regulations, policies, and ethical standards.
Recommended Qualifications:
- Minimum of 6-10 years experience in technology risk and/or cyber risk management, information security, information technology or related field
- Previous experience supporting risk management, compliance, governance, or controls related roles.
- Demonstrated experience performing independent technology, cyber, data, and operational risk assessments, monitoring reviews, control evaluations, and assurance activities within large, complex financial services or highly regulated environments. Experience developing risk assessments, testing strategies, workpapers, observations, issues, and executive-level reporting.
- Strong understanding of technology, cyber, cloud, data, and AI risk management practices, including governance, risk assessment, control design, control testing, issue management, and remediation validation.
- Knowledge of Artificial Intelligence (AI), Generative AI (GenAI), Machine Learning (ML), AI Agents, Agentic AI, digital assistants, and AI-enabled business processes, including associated risks related to model governance, privacy, security, data management, regulatory compliance, resiliency, explainability, and responsible AI practices.
- Knowledge of products within the coverage area, including an understanding of current and emerging trends as well as the ability to apply understanding of the business impacts of technical contributions.
- Experience in technology and cyber risk assessments, metrics, enterprise technology services, risks, and controls within globally complex, dispersed and diverse organizations.
- In-depth knowledge of technology risks and controls across various information system architecture and engineering domains including: data protection, identity and access management, vulnerability management, network security, endpoint security, logging and monitoring, incident management, and third-party management; preferred expertise in application development, software development lifecycle (SDLC), identity and access management, cloud technologies, enterprise and technology architecture, end-user computing (EUC), technology governance, AI governance, AI-enabled software development, and AI/Agentic AI control frameworks.
- Proficient in industry standard risk management frameworks (including ISO27001, COBIT, TOGAF and CRI for example), and an in-depth understanding of technology and cyber risk mitigation strategies.
- Consistently demonstrates clear and concise written and verbal communication skills
- Developed communication and diplomacy skills are required to guide, influence, and convince others, in particular colleagues in other areas and occasional external customers. Requires good analytical skills to filter, prioritize and validate potentially complex material from multiple sources.
Education:
- Bachelor's/University degree, Master's degree preferred
Relevant certifications in CISM, CRISC, AAIA, AAIR, CISSP and CISA a plus *
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Job Family Group:
Risk Management -
Job Family:
Operational Risk -
Time Type:
Full time -
Primary Location:
Tampa Florida United States -
Primary Location Full Time Salary Range:
$113,840.00 - $170,760.00
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
Analytical Thinking, Controls Lifecycle, Credible Challenge, Governance, Policy, Procedure, and Regulation, Risk Management Lifecycle. -
Other Relevant Skills
For complementary skills, please see above and/or contact the recruiter. -
Anticipated Posting Close Date:
Oct 13, 2026 -
Automated Processing and AI
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 $113K-$170K range is below the median for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% 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 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 $211,950 based on 13,048 positions with disclosed compensation. This role's midpoint ($142K) sits 33% below the category median. Disclosed range: $113K to $170K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Citi AI Hiring
Citi has 44 open AI roles right now. They're hiring across AI/ML Engineer, Data Engineer, Data Scientist, AI Software Engineer. Positions span Jersey City, NJ, US, Jersey City, NJ, New York, NY. Compensation range: $130K - $300K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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