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

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

+1 more

$157K/yr - $236K/yr

Banking

Finance

Responsibilities

• 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
• 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
• 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
• 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
• 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
• 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
• Track and report technical delivery outcomes (model performance, latency, accuracy, guardrail effectiveness) against defined engineering and business benchmarks
• 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
• 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
• Provide technical feasibility assessments and effort estimates to business stakeholders and analytics leadership to support prioritization of high-impact AI solutions
• 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

Preferred

• Master's degree preferred
• Ph.D. a plus

Benefits

• For eligible employees, discretionary and formulaic incentive and retention awards
• Medical, dental & vision coverage
• 401(k)
• Life, accident, and disability insurance
• Wellness programs
• Paid time off packages, including planned time off (vacation), unplanned time off (sick leave), and paid holidays
• Glassdoor
• 3.6
• Global bank providing financial services.
• New York, New York, USA
• https://www.citi.com
• Distribution of Different Job Fields Receiving Sponsorship
• Represents job field similar to this job
• Engineering and Development
• Accounting and Finance
• Management and Executive
• Customer Service and Support
• Product Management
• Legal and Compliance
• Sales
• Human Resources
• Marketing
• Public Sector and Government
• Creatives and Design
• Trends of Total Sponsorships
• *2026
• (1109)
• 2025
• (1386)

Qualifications

• 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
• Generative AI, LLMs, RAG, Agentic AI, prompt engineering, LLM fine-tuning, NLP, supervised/unsupervised learning, mathematical optimization & decision science
• MLOps/LLMOps, cloud-native AI platforms, big data, distributed computing, Spark, Python, SQL — strong hands-on coding ability expected
• Building adversarial testing suites, guardrails, regulatory testing frameworks, and near real-time model monitoring solutions
• SDLC, version control (Bitbucket/GitHub), JIRA, CI/CD and DevOps practices
• 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
• 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
• Bachelor's degree in a quantitative discipline (Statistics, Mathematics, Computer Science, Engineering, or Economics) required
• Master's degree preferred
• Ph.D. a plus

Required skills

• Agentic AI, Banking Operations, Big Data, Bitbucket/GitHub, CI/CD, Credit Card Economics, DevOps, Distributed Computing, Fair Lending, Financial Services, Generative AI, JIRA, LLM Fine-Tuning, Large Language Models (LLMs), MLOps/LLMOps, Mathematical Optimization and Decision Science, Model Governance, Model Risk Management, Natural Language Processing (NLP), Prompt Engineering, Python, Retrieval-Augmented Generation (RAG), SQL, Spark, Supervised and Unsupervised Learning

Preferred skills

• Master's degree preferred, Ph.D. a plus

Experience

• 6+ years