Vice President – Generative AI Technical Expert, Data Science & Artificial Intelligence — Hybrid

About the Role

We are looking for a hands-on, technically deep Generative AI expert to drive Partner AI Development & Delivery and GenAI Model Governance from an individual-contributor, technical-leadership standpoint. This role sits at the intersection of Generative AI innovation, enterprise model governance, and regulatory compliance — building and delivering high-visibility AI solutions that directly shape business outcomes at scale.

You will work as a subject-matter expert embedded within a broader team, partnering with engineers, data scientists, and stakeholders across Operations, Marketing, Products, Technology, Model Risk, Fair Lending, and Legal. Your focus will be on architecting, building, and validating GenAI solutions, while contributing technical guidance that supports expense reduction, revenue growth, customer experience improvement, compliance transformation, and enterprise GenAI capability buildout.

What You'll Deliver

1. Banking Operations AI Automation Design and build GenAI-powered automation across front, mid, and back office banking operations — including call center IVR, Agent Assist, Collections, and other operational functions. Apply deep technical expertise in LLMs and applied ML, combined with banking domain knowledge, to solve complex, cross-functional problems at the engineering level.

2. Foundation GenAI Capabilities Build and maintain enterprise-wide foundational GenAI capabilities, including LLM fine-tuning pipelines and semantic layer development, enabling scalable, data-driven decision-making across business functions.

3. GenAI Solution Development Take individual technical ownership of the AI solution lifecycle — from prototyping to production — for high-priority use cases including Personalized Next Best Action decisioning, Campaign Analytics, and Conversational & Contextual Business Insights. Write and review code, design model architectures, and validate performance.

4. Analytics Workflow Transformation Build AI-powered tools and accelerators that improve analytics workflows and productivity. Prototype and evolve AI-enhanced modeling techniques (e.g., agentic workflows, RAG pipelines) to improve development speed and precision.

5. GenAI Model Portfolio Support Provide hands-on technical support across a GenAI model portfolio — tuning, upgrades, evaluation, and maintenance. Act as a technical contributor to Model Sponsor documentation, providing the technical evidence, testing artifacts, and model documentation required for governance sign-off under Model Risk Policy.

6. Enterprise GenAI Capabilities Build and implement firm-wide GenAI technical infrastructure: adversarial testing harnesses, regulatory testing frameworks, guardrails, and near real-time monitoring pipelines. Contribute technical input into AI governance policy development alongside the second line of defense.

7. Measurable Technical Delivery Track and report technical delivery outcomes (model performance, latency, accuracy, guardrail effectiveness) against defined engineering and business benchmarks.

How You'll Deliver

Apply Deep Technical Expertise Bring hands-on proficiency in GenAI/LLM engineering to solve enterprise-grade problems, staying current with emerging techniques (agentic AI, RAG, fine-tuning, prompt engineering) and applying them pragmatically to production systems.

Collaborate with Central AI and Business Teams Work as a technical partner with the firm's Central AI organization and business units, contributing architecture recommendations and technical designs that support process redesign and measurable change.

Contribute to Prioritization Discussions Provide technical feasibility assessments and effort estimates to business stakeholders and analytics leadership to support prioritization of high-impact AI solutions.

Uphold a Strong Controls Mindset Apply and follow control processes across Information Security, Platform Security, and Data Security in all technical work, ensuring GenAI solutions are built and deployed securely and compliantly.

Qualifications

Experience

• 6+ years in AI/ML, data science, or applied engineering roles within financial services or a similarly regulated industry
• Demonstrated hands-on experience building and deploying Generative AI/LLM solutions in production environments
• Practical experience supporting model risk management, regulatory remediation, and model documentation/governance processes
• Experience working with AI/ML model lifecycles, including tuning, evaluation, and upgrades
• Working knowledge of core banking data domains — marketing, customer management, call center operations, collections, complaints, and financial controls — sufficient to translate business requirements into technical solutions
• Familiarity with credit card economics, customer behavior, and product strategy as they relate to data/AI use cases
• Exposure to Model Risk Management, Fair Lending, and Compliance requirements in a regulated financial institution

Technical Skills

• GenAI & ML: Generative AI, LLMs, RAG, Agentic AI, prompt engineering, LLM fine-tuning, NLP, supervised/unsupervised learning, mathematical optimization & decision science
• Engineering & Infrastructure: MLOps/LLMOps, cloud-native AI platforms, big data, distributed computing, Spark, Python, SQL — strong hands-on coding ability expected
• Governance & Testing: Building adversarial testing suites, guardrails, regulatory testing frameworks, and near real-time model monitoring solutions
• Tooling: SDLC, version control (Bitbucket/GitHub), JIRA, CI/CD and DevOps practices
• Regulatory Awareness: Working knowledge of SR 11-7, SR 26-2, OCC AI guidance, CFPB, Fair Lending, ECOA as they apply to AI/ML deployment within a regulated institution

Communication & Collaboration

• Ability to work effectively as an embedded technical expert within cross-functional, matrixed teams
• Strong stakeholder communication skills — able to explain technical concepts, trade-offs, and model behavior clearly to non-technical partners
• Solid documentation and technical writing skills to support model governance and audit requirements
• Comfortable presenting technical findings and demos to mid-to-senior audiences

Education

• Bachelor's degree in a quantitative discipline (Statistics, Mathematics, Computer Science, Engineering, or Economics) required
• Master's degree preferred; Ph.D. a plus

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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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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.

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