Vice President – Generative AI Technical Expert, Data Science & Artificial Intelligence — Hybrid
maybe. However we must not invent sections not in list? The instruction: use
for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). It says use those for section headings; we can also have other headings? Probably stick to those. We'll include About the role (intro paragraph), Responsibilities (What You'll Deliver bullet list), Qualifications (Experience bullet list), Skills (Technical Skills bullet list), Benefits (benefits description), Pay (salary range), Schedule (Full time). Also maybe include Education as part of Qualifications? Could be separate but we can include under Qualifications. We'll preserve all substantive info.
We need to format lists with - . Use
for prose.
We must not include boilerplate like EEO, automated processing, etc. Also remove duplicated lines like "Most Relevant Skills Please see the requirements listed above." etc. Also remove "Other Relevant Skills". Also remove "Anticipated Posting Close Date". Also remove "Job Family Group", etc. Also remove "Primary Location Full Time Salary Range:" line? We'll extract salary range and benefits.
Let's craft.
First, About the role: combine the introductory paragraphs: "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."
We'll put that in
.
Responsibilities: list each bullet under "What You'll Deliver". Items:
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- Measurable Technical Delivery: Track and report technical delivery outcomes (model performance, latency, accuracy, guardrail effectiveness) against defined engineering and business benchmarks.
Also "How You'll Deliver" could be considered responsibilities as well but maybe we treat as part of responsibilities? The instruction: Use
- for any list of duties/requirements/benefits. So we can combine all duty-like bullet points under Responsibilities. The "How You'll Deliver" section includes bullet points: Apply Deep Technical Expertise, Collaborate with Central AI and Business Teams, Contribute to Prioritization Discussions, Uphold a Strong Controls Mindset. Those are also responsibilities/duties. We'll include them as well.
Thus Responsibilities list will be longer.
Qualifications: list bullet points from "Qualifications" section: 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.
Education: Bachelor's degree in a quantitative discipline (Statistics, Mathematics, Computer Science, Engineering, or Economics) required; Master's degree preferred; Ph.D. a plus.
We could put Education under Qualifications as separate bullet points.
Skills: list from "Technical Skills" and maybe also "Communication & Collaboration". We'll separate maybe into Technical Skills and Soft Skills but we only have Skills heading. We'll include all.
Technical Skills bullets:
- 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
Benefits: from paragraph: "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."
We'll put that as
under Benefits.
Pay: salary range: "$157,040.00 - $235,560.00". Also mention discretionary and formulaic incentive and retention awards maybe part of Pay? Could include but we already have benefits. We'll just put salary range.
Schedule: "Full time".
We must not include any filler like "commensurate with experience". Not present.
We must not include duplicated lines. Ensure we don't include "Most Relevant Skills" etc.
Now produce HTML fragment:
About the role
...
Responsibilities
- ...
...
Qualifications
- ...
...
Skills
- ...
...
Benefits
...
Pay
$157,040.00 - $235,560.00
Schedule
Full time
Make sure no extra whitespace? It's fine.
Let's craft the content precisely.
About the role paragraph: combine sentences.
We'll write:
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.
Now Responsibilities list items. We'll combine all bullet points from "What You'll Deliver" and "How You'll Deliver".
Let's list:
- 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
for prose. We must not include boilerplate like EEO, automated processing, etc. Also remove duplicated lines like "Most Relevant Skills Please see the requirements listed above." etc. Also remove "Other Relevant Skills". Also remove "Anticipated Posting Close Date". Also remove "Job Family Group", etc. Also remove "Primary Location Full Time Salary Range:" line? We'll extract salary range and benefits. Let's craft. First, About the role: combine the introductory paragraphs: "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." We'll put that in
. Responsibilities: list each bullet under "What You'll Deliver". Items: - 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. - 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. - 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. - 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. - 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. - 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. - Measurable Technical Delivery: Track and report technical delivery outcomes (model performance, latency, accuracy, guardrail effectiveness) against defined engineering and business benchmarks. Also "How You'll Deliver" could be considered responsibilities as well but maybe we treat as part of responsibilities? The instruction: Use
- for any list of duties/requirements/benefits. So we can combine all duty-like bullet points under Responsibilities. The "How You'll Deliver" section includes bullet points: Apply Deep Technical Expertise, Collaborate with Central AI and Business Teams, Contribute to Prioritization Discussions, Uphold a Strong Controls Mindset. Those are also responsibilities/duties. We'll include them as well.
Thus Responsibilities list will be longer.
Qualifications: list bullet points from "Qualifications" section: 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.
Education: Bachelor's degree in a quantitative discipline (Statistics, Mathematics, Computer Science, Engineering, or Economics) required; Master's degree preferred; Ph.D. a plus.
We could put Education under Qualifications as separate bullet points.
Skills: list from "Technical Skills" and maybe also "Communication & Collaboration". We'll separate maybe into Technical Skills and Soft Skills but we only have Skills heading. We'll include all.
Technical Skills bullets:
- 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
Benefits: from paragraph: "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."
We'll put that as
under Benefits. Pay: salary range: "$157,040.00 - $235,560.00". Also mention discretionary and formulaic incentive and retention awards maybe part of Pay? Could include but we already have benefits. We'll just put salary range. Schedule: "Full time". We must not include any filler like "commensurate with experience". Not present. We must not include duplicated lines. Ensure we don't include "Most Relevant Skills" etc. Now produce HTML fragment:
About the role
...
Responsibilities
- ... ...
Qualifications
- ... ...
Skills
- ... ...
Benefits
...
Pay
$157,040.00 - $235,560.00
Schedule
Full time
Make sure no extra whitespace? It's fine. Let's craft the content precisely. About the role paragraph: combine sentences. We'll write: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.
Now Responsibilities list items. We'll combine all bullet points from "What You'll Deliver" and "How You'll Deliver". Let's list: - 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