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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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Services provides global solutions that help corporations, financial institutions, public sector and commercial clients optimize operations and drive their business forward. Through our five business lines – Liquidity Management Services, Payments, Trade \& Working Capital Solutions, Investor Services and Issuer Services \- we provide cash management, payments/receivables solutions, working capital solutions, post\-trade securities services and issuer services across Citi’s global network.
Citi is looking for an AI Agentic Development \& Implementation \- Director to lead the strategic direction and end\-to\-end delivery of enterprise agentic AI capabilities within Platform and Data Services (PDS), a foundational function that powers client servicing, operational excellence, and business growth across the franchise. In this role, you will shape the target vision for agentic AI, translate priority use cases into scalable platform capabilities, and partner across Product, Technology, Architecture, Data Science, Engineering, Risk, and Business teams to build the next generation of intelligent, AI\-enabled services at global scale. This is a rare opportunity to hold strategic, product, and execution accountability simultaneously — defining how agentic AI is built, governed, and industrialized across one of the world's leading financial institutions.
Responsibilities
- Define and execute the Agentic AI strategy and roadmap: Shape the target vision, priorities and implementation roadmap for agentic AI across Services, aligned with business demand, the core platform strategy, and enterprise AI standards.
- Translate priority use cases into scalable agentic solutions: Partner with Product, Technology, Data Science and Architecture teams to assess use cases and define the required agent patterns, workflows and capabilities, including planning, orchestration, memory, tools and connectors, multi\-agent coordination and human oversight.
- Lead agentic implementation and capability industrialization: Drive rapid experimentation, prototyping and implementation technology teams, forward\-deployed engineers and strategic technology partners, converting successful concepts into reusable platform capabilities and production\-ready design patterns.
- Embed responsible AI, controls and operational resilience: Implement and oversee enterprise agentic design standards covering permissions, security, model and tool selection, evaluation, observability, traceability, exception management, human escalation and lifecycle governance; partner with Risk, Compliance, Model Risk and Architecture to support controlled deployment.
- Establish the agentic operating model and demonstrate value: Create repeatable methods, playbooks and governance for moving agentic use cases from discovery through production; build internal capability through training and knowledge sharing, and track adoption, performance, reuse and measurable business outcomes to inform future investment and roadmap decisions.
*Who We're Looking For*
Experience \& Qualifications
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The ideal candidate combines deep product management expertise with an informed, commercial understanding of AI and the leadership presence to influence at every level of the organization.
Required Skills \& Competencies
- 10\+ years of product management, platform strategy, or enterprise technology leadership, with demonstrated experience delivering complex, enterprise\-scale products.
- Proven experience defining and implementing AI, GenAI, or Agentic AI solutions, including translating business problems into scalable platform capabilities.
- Deep understanding of Agentic AI concepts, including orchestration, multi\-agent systems, planning, memory, retrieval\-augmented generation (RAG), tool use, MCPs, human\-in\-the\-loop design, and workflow automation.
- Strong enterprise platform and architecture knowledge, including APIs, cloud\-native architectures, microservices, event\-driven systems, integrations, and reusable platform services.
- Exceptional product management capability, including roadmap development, product lifecycle management, business case development, prioritization, and value realization.
- Strong commercial and strategic mindset, with the ability to evaluate AI investments, prioritize opportunities, and balance innovation with measurable business outcomes.
- Experience operating within regulated environments, with knowledge of Responsible AI, model governance, security, privacy, operational resilience, and enterprise risk management.
- Outstanding stakeholder management and executive communication skills, with the ability to influence Product, Technology, Architecture, Engineering, Operations, Risk, and senior business leaders across a highly matrixed organization.
- Demonstrated ability to lead cross\-functional teams, manage ambiguity, and drive execution across multiple concurrent strategic initiatives.
- Strong analytical and structured problem\-solving skills, with the ability to simplify complex technical concepts into clear business recommendations and implementation plans.
Preferred Skills
- Experience delivering enterprise AI platforms or developer platforms, forward deployed engineers including familiarity with large language models, small language models, prompt engineering, evaluation frameworks, and AI observability tooling.
- Experience working directly with AI engineering teams, forward\-deployed engineers, or AI platform organizations to take concepts through to scaled delivery.
- Track record of leading AI transformation programs, platform modernization initiatives, or enterprise operating model redesign.
- Experience engaging directly with clients on AI strategy, co\-creation, or solution design, and working with Architecture Review Boards or enterprise technology governance bodies.
- Background in financial services, payments, treasury, securities services, or transaction banking.
What We Offer
At Citi, you will have the opportunity to work at the intersection of strategy, technology, and global financial services — shaping how enterprise AI is built and scaled across a business that spans more than 160 countries. This is a high\-impact leadership role with genuine scope to define how agentic AI evolves across the franchise, supported by a collaborative, performance\-driven environment and a comprehensive set of benefits.
- Strategic ownership of one of Citi's most significant technology transformation agendas, with direct influence over enterprise AI direction and investment.
- Hybrid working model with 3 days in the office and 2 days working remotely, supporting a sustainable balance between collaboration and focused delivery.
- Access to structured learning and development programs to deepen both technical and leadership capability as the AI landscape continues to evolve.
- Competitive compensation and financial wellbeing benefits, including access to long\-term financial planning support.
- Wellbeing and family support resources, including programs designed to support physical, mental, and financial health.
- The opportunity to operate at global scale — partnering with world\-class teams across technology, data science, product, and business to deliver outcomes that matter to clients and the broader institution.
Apply now to lead the design and delivery of enterprise agentic AI capabilities that will define how Citi serves clients and operates at scale for years to come.
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Job Family Group:
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Product Management and Development
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Job Family:
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Product Management
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Time Type:
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Primary Location:
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New York New York United States
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Primary Location Full Time Salary Range:
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$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
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Please see the requirements listed above.
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Other Relevant Skills
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For complementary skills, please see above and/or contact the recruiter.
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Anticipated Posting Close Date:
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Aug 19, 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 $170K-$300K range is above the median for AI Agent Developer roles in our dataset (median: $200K across 33 roles with salary data).
View full AI Agent Developer salary data →Role Details
About This Role
AI Agent Developers build autonomous systems that can reason, plan, and take actions. They design multi-step workflows, tool-use frameworks, and orchestration layers that let LLMs interact with external systems. This is the frontier of applied AI engineering.
Agent development is where the most interesting (and hardest) problems in applied AI live right now. Making an LLM answer a question is straightforward. Making it reliably execute a 15-step workflow that involves calling APIs, reading databases, making decisions, and recovering from errors is an unsolved problem. You're building systems that have to work despite the fact that the underlying model is non-deterministic.
Across the 4,317 AI roles we're tracking, AI Agent Developer positions make up 1% of the market. At Citi, this role fits into their broader AI and engineering organization.
AI Agent Developer is one of the newest and fastest-growing AI role categories. The market is early but accelerating as companies move beyond simple chatbots toward AI systems that can take real actions. Compensation is high because the skill set is rare and the business impact is potentially enormous.
What the Work Looks Like
A typical week includes: designing the action space and tool definitions for a new agent use case, debugging why the agent chose the wrong action sequence on a specific input, building evaluation frameworks that test agent reliability across hundreds of scenarios, optimizing the prompt chain for cost and latency, and implementing safety guardrails to prevent the agent from taking destructive actions. The work is equal parts engineering and empirical science.
AI Agent Developer is one of the newest and fastest-growing AI role categories. The market is early but accelerating as companies move beyond simple chatbots toward AI systems that can take real actions. Compensation is high because the skill set is rare and the business impact is potentially enormous.
Skills Required
Deep experience with LLM APIs and agent frameworks (LangChain, CrewAI, AutoGen). Strong understanding of prompt engineering, function calling, and error handling for non-deterministic systems. Python is standard. Experience with orchestration patterns, state management, and workflow engines adds significant value.
The best agent developers think like systems engineers. They design for failure modes, build observability into every step, and understand that agent reliability is the product. Expertise in evaluation methodology for non-deterministic systems is the differentiator. Can you measure whether your agent works 'well enough'? Can you find the edge cases where it breaks?
Look for roles that describe specific agent use cases, mention evaluation methodology, and talk about production deployment. Early-stage companies exploring agents can be exciting, but be prepared for ambiguity. The most valuable roles are at companies that have already shipped a v1 and need to make it reliable.
Compensation Benchmarks
AI Agent Developer roles pay a median of $240,000 based on 96 positions with disclosed compensation. Director-level AI roles across all categories have a median of $274,554. Disclosed range: $170K to $300K.
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 Agent Developer roles include Software Engineer, LLM Engineer, Prompt Engineer.
From here, career progression typically leads toward AI Architect, Principal Engineer, Head of AI Engineering.
Build agents. That's the portfolio. Take an open-source agent framework, build something that completes a non-trivial multi-step task, evaluate it rigorously, and document what you learned about reliability, cost, and failure modes. The field is new enough that practical experience counts for more than credentials.
What to Expect in Interviews
Interviews focus on systems thinking and reliability engineering. Expect questions about agent architecture: how you'd design a multi-step workflow with error recovery, how you'd evaluate agent performance, and how you'd prevent agents from taking destructive actions. Coding exercises often involve building a simple agent with tool use and evaluating its behavior across different scenarios. Discussion of safety and guardrails is increasingly common.
When evaluating opportunities: Look for roles that describe specific agent use cases, mention evaluation methodology, and talk about production deployment. Early-stage companies exploring agents can be exciting, but be prepared for ambiguity. The most valuable roles are at companies that have already shipped a v1 and need to make it reliable.
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).
AI Agent Developer is one of the newest and fastest-growing AI role categories. The market is early but accelerating as companies move beyond simple chatbots toward AI systems that can take real actions. Compensation is high because the skill set is rare and the business impact is potentially enormous.
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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