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About This Role
Posted Date
6/02/2026
Description
Citi is looking for a Director\-level technology leader to drive the strategy, design, and delivery of enterprise\-scale AI\-enabled platforms and shared service capabilities across five business verticals within our global Financial Services organization. Based in Jersey City, this is a high\-impact horizontal leadership role at the intersection of AI innovation, platform engineering, and enterprise architecture shaping how technology is built and scaled across Citi's most critical business lines. If you are a senior engineering leader who thrives in complex, matrixed environments and have a passion for building shared platforms that power real business outcomes, this role offers the scope, influence, and resources to make a lasting mark.
#### Responsibilities
- Lead the horizontal integration of enterprise platforms and shared service capabilities across five business verticals, ensuring scalable, reusable solutions that accelerate delivery and reduce duplication.
- Architect and evolve AI\-driven systems including multi\-agent frameworks, LLM orchestration pipelines, and vector\-database\-backed retrieval systems integrated into enterprise\-grade financial services applications.
- Define and enforce engineering standards across the full software development lifecycle — design, coding, testing, debugging, and deployment — promoting consistency and quality across all shared platforms.
- Lead and deliver local staff forums, present global, regional, and location news and updates to engage team in the progress of organization
- Contribute to defining and implementing best practices and processes for the department and ensure transparency and consistency across teams
- Appropriately assess risk when business decisions are made, demonstrating particular consideration for the firm's reputation and safeguarding Citigroup, its clients and assets, by driving compliance with applicable laws, rules and regulations, adhering to Policy, applying sound ethical judgment regarding personal behavior, conduct and business practices, and escalating, managing and reporting control issues with transparency, as well as effectively supervise the activity of others and create accountability with those who fail to maintain these standards.
- Act as the primary technology partner to senior business leaders across verticals — influencing strategic decisions, driving consensus, and translating complex technical capabilities into business value.
- Identify and mitigate cross\-platform risks, maintain disaster recovery plans, and ensure all platforms meet regulatory compliance and governance standards within the Financial Services environment.
#### Required Qualifications \& Skills
- 15\+ years of experience in applications development, with deep expertise in enterprise application design, systems analysis, and large\-scale platform delivery.
- 8–10\+ years leading engineering teams in senior management roles, with demonstrated accountability for global, distributed teams and complex cross\-functional programs.
- Hands\-on expertise in AI and LLM technologies, including LLM orchestration frameworks (LangChain, LlamaIndex), prompt engineering techniques (ReAct, Chain\-of\-Thought), and multi\-agent system design.
- Strong command of service\-based and microservices architecture, API\-first design, distributed systems, and building shared horizontal platforms that serve multiple business lines.
- Proven ability to operate effectively in matrixed, horizontal leadership roles — influencing senior stakeholders and driving alignment across diverse business and technology teams.
- Financial Services domain expertise, including familiarity with financial products, regulatory compliance requirements, and governance frameworks.
#### Beneficial Qualifications \& Skills
- Experience in formal horizontal platform leadership roles — owning shared technology capabilities across multiple business verticals within a large enterprise.
- Familiarity with vector databases, memory and retrieval systems, and their application within enterprise AI architectures.
- Background in DevOps and platform engineering practices, including CI/CD pipelines, infrastructure\-as\-code, and cloud\-native delivery models.
Education:
- Bachelor’s degree/University degree or equivalent experience
#### What We Offer
At Citi, we invest in our leaders. In this role, you will have strategic ownership, global reach, and organizational influence to shape how AI and enterprise platforms are built and scaled across one of the world's leading financial institutions — while being supported by a culture that values your growth, wellbeing, and long\-term success.
- Hybrid working model — 3 days in the office and 2 days working remotely, giving you flexibility without sacrificing collaboration.
- Strategic leadership scop own a multi\-million\-dollar technology portfolio and lead global engineering teams, with direct influence on enterprise\-wide platform decisions.
- Access to cutting\-edge AI and platform engineering challenges at scale, working at the forefront of applied AI in Financial Services.
- Continuous learning and development opportunities, including access to leadership programs, technical communities, and industry networks.
- Competitive compensation and financial wellbeing benefits, designed to reflect the seniority and impact of this role.
- A globally collaborative environment, connecting you with technology and business leaders across Citi's worldwide network.
- Wellbeing and family support programs, helping you maintain balance and thrive both professionally and personally.
Build the AI\-powered platforms that drive one of the world's most influential financial institutions apply today and bring your vision to Citi.
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#### Job Family Group:
Technology
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#### Job Family:
Applications Development
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#### Time Type:
Full time
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#### Primary Location:
Jersey City New Jersey United States
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#### Primary Location Full Time Salary Range:
$170,000\.00 \- $300,000\.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
Please see the requirements listed above.
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#### Other Relevant Skills
For complementary skills, please see above and/or contact the recruiter.
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#### Anticipated Posting Close Date:
Jun 08, 2026
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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.*
*If you are a person with a disability and need a reasonable accommodation to use our search tools and/or apply for a career opportunity review* *Accessibility at Citi**.*
*View Citi’s* *EEO Policy Statement* *and the* *Know Your Rights* *poster.*
Salary
170,000\.00 \- 300,000\.00 Annual
Type
Full\-time
Salary Context
This $170K-$300K range is above the 75th percentile for AI Product Manager roles in our dataset (median: $189K across 161 roles with salary data).
View full AI Product Manager salary data →Role Details
About This Role
AI Product Managers define what AI features get built and why. They translate business problems into ML-solvable tasks, work with engineering to scope model requirements, and own the metrics that determine if an AI feature is working. The role requires a rare combination of technical fluency and product instinct.
Unlike traditional product management, AI PM work involves managing uncertainty at a fundamental level. Your model might work 90% of the time. What happens the other 10%? What's the user experience when the AI is wrong? How do you measure 'good enough' for a probabilistic system? These questions don't have easy answers, and the AI PM is the person responsible for finding them.
Across the 3,823 AI roles we're tracking, AI Product Manager positions make up 5% of the market. At Information Technology Senior Management Forum, this role fits into their broader AI and engineering organization.
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
What the Work Looks Like
A typical week includes: reviewing model evaluation results with the ML team, defining success metrics for a new AI feature, conducting user research on how customers respond to AI-generated outputs, writing product requirements that include accuracy thresholds and fallback behaviors, and presenting the AI roadmap to leadership. You're the translator between technical capability and business value.
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
Skills Required
Technical fluency with ML concepts is essential, though you won't be writing models. Expect to understand training data, evaluation metrics, model limitations, and responsible AI practices. SQL and basic Python are increasingly expected. Experience with A/B testing, data analysis, and product analytics is baseline. Understanding LLM capabilities and limitations is now a core requirement.
The differentiator is AI-specific product thinking: knowing when to use ML vs. heuristics, understanding the cost of training data collection, designing graceful degradation for model failures, and building products that improve with usage data. Experience with AI safety, bias mitigation, and responsible AI deployment is increasingly important.
Strong postings describe specific AI products the PM will own, mention the ML team structure, and talk about measurement methodology. Look for companies that have already shipped AI features. Roles at companies that are 'exploring AI' often mean you'll spend a year defining the strategy before any building happens.
Compensation Benchmarks
AI Product Manager roles pay a median of $213,800 based on 583 positions with disclosed compensation. Director-level AI roles across all categories have a median of $247,800. This role's midpoint ($235K) sits 10% above the category median. Disclosed range: $170K to $300K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Information Technology Senior Management Forum AI Hiring
Information Technology Senior Management Forum has 34 open AI roles right now. They're hiring across AI Engineering Manager, Data Scientist, AI/ML Engineer, Data Engineer. Positions span San Jose, CA, US, Jersey City, NJ, US, McLean, VA, US. Compensation range: $167K - $335K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 median).
Career Path
Common paths into AI Product Manager roles include Product Manager, Data Analyst, Technical Program Manager.
From here, career progression typically leads toward Director of AI Product, VP Product, Head of AI.
The most effective path is PM experience plus self-directed AI education. Take Andrew Ng's courses, build a small ML project, and learn enough Python to read model evaluation code. The goal isn't to become an ML engineer. It's to have credibility in technical conversations and to understand what's possible, what's hard, and what's a bad idea.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Strong postings describe specific AI products the PM will own, mention the ML team structure, and talk about measurement methodology. Look for companies that have already shipped AI features. Roles at companies that are 'exploring AI' often mean you'll spend a year defining the strategy before any building happens.
AI Hiring Overview
The AI job market has 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 roles).
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
The AI Job Market Today
The AI job market spans 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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