AI ML Engineering Lead - Vice President - AI for Payments

We need to output HTML fragment with sections: About the role? Actually we need to use

for section headings like About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. We must include sections only if posting has real specific content. We have content: Job Description (intro) maybe "About the role". Responsibilities list. Required Qualifications, Capabilities, And Skills (maybe combine as Requirements/Qualifications/Skills). Preferred Qualifications also maybe part of Skills? We'll need to decide. We must not repeat job title as heading. No Job ID. Remove boilerplate: EEO, equal-opportunity, scam-warning, navigation, cookie, legal, application instructions. Also remove "About Us", "About The Team" maybe? Those are not required sections but could be considered "About the role"? The instruction: Use

for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). So we can include About the role (intro), Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule only if content exists. We have no explicit Pay or Schedule info. So we should not include those sections. Benefits: The posting mentions "competitive total rewards package including base salary... benefits include comprehensive health care coverage, on-site health and wellness centers, a retirement savings plan, backup childcare, tuition reimbursement, mental health support, financial coaching and more." That's benefits info. So we can include Benefits section. We have Responsibilities: list. We have Required Qualifications, Capabilities, And Skills: list. We have Preferred Qualifications, Capabilities, And Skills: list. We could combine Required and Preferred into Requirements and Skills? But we have separate headings: Requirements, Qualifications, Skills. Could map Required Qualifications to Requirements and Qualifications, Preferred to Skills? Or we could have Requirements (required qualifications), Qualifications (maybe same), Skills (preferred). But we must not create thin sections; better to have a few substantive sections. Perhaps we can have: About the role, Responsibilities, Requirements (combining required and preferred), Benefits. Or we could have Requirements (required), Qualifications (preferred), Skills (maybe not needed). But we need to follow instruction: use those headings if content exists. It says "e.g." not mandatory to use all. So we can choose which to include. We need to preserve ALL substantive information; do not invent or summarize away detail. So we must keep all details from responsibilities, required qualifications, preferred qualifications, benefits. We can't drop any. Thus we need sections that can hold all that info without losing detail. Could have:

About the role

... intro paragraph(s) ...

Responsibilities

... list items ...

Required Qualifications

... list ...

Preferred Qualifications

... list ...

Benefits

... list ...

We could also have Skills but not needed. We must not include Pay or Schedule. We must not repeat job title as heading. The intro may contain the role description; we can keep as About the role. We must remove boilerplate: "About Us", "About The Team", EEO statements, etc. Also remove "Additional details about total compensation and benefits will be provided during the hiring process." That's maybe part of benefits but it's generic; we could keep it? It's not specific detail; it's a placeholder. According to rule: never emit a heading followed by filler/placeholder text (e.g., "commensurate with experience", "not specified", "N/A", "varies", "TBD"). That sentence is generic but not exactly placeholder; still it's about additional details will be provided later. Might be considered filler. Safer to omit that sentence. We'll keep the specific benefits list. We also need to remove "We recognize that our people are our strength..." EEO etc. Remove. Now craft HTML. We need to preserve all substantive info from responsibilities list: Let's extract. Responsibilities bullet points from posting: - Partner with senior business stakeholders to frame problems, define success metrics, and align AI/ML roadmaps to priorities - Lead architecture, design, and end-to-end delivery of enterprise AI/ML solutions for Wholesale Payments Operations - Write clean, performant, production-quality code and set engineering standards across the team - Champion modern software development life cycle (SDLC), continuous integration and continuous delivery (CI/CD), and DevOps practices - Deploy and operate AI/ML services on AWS at scale - Apply advanced techniques including data/text mining, document analysis, classification, optical character recognition (OCR), natural language processing (NLP), and LLM workflows (including retrieval-augmented generation and fine-tuning) - Design and implement scalable, secure data pipelines to support model training and inference - Define and enforce MLOps, model governance, monitoring, and responsible AI practices; represent the team in architecture and risk forums - Evaluate model performance in production, including drift management and reproducibility - Mentor engineers, conduct code and design reviews, and support recruiting and talent development Now Required Qualifications list: - Master's degree in Computer Science, Engineering, Mathematics, or a related quantitative field - 6 years of professional software engineering experience delivering production systems - 4 years of advanced Python development in production environments, including use of AI-assisted coding tools (e.g., GitHub Copilot, Claude Code) to improve throughput while preserving code quality - 4 years of hands-on experience designing and deploying production machine learning systems on Amazon Web Services (AWS) (for example: SageMaker, Lambda, ECS/EKS, S3) - Demonstrated experience delivering AI/ML solutions with measurable business outcomes at scale - Experience with object-oriented design, distributed systems, and performance engineering - Demonstrated experience building and deploying LLM-based applications, including retrieval-augmented generation and fine-tuning workflows - Hands-on experience in natural language processing (NLP), computer vision, optical character recognition (OCR), or document AI solutions in production - Experience implementing MLOps practices using tools such as MLflow, Kubeflow, Airflow, feature stores, or model registries - Demonstrated experience mentoring engineers and driving execution against multi-quarter roadmaps - Strong communication skills, including translating business needs into technical deliverables for senior (Note: last bullet truncated "for senior" maybe "for senior stakeholders". We'll keep as is.) Preferred Qualifications list: - Experience delivering AI/ML solutions in wholesale payments, transaction banking, or financial services - Experience with model risk management frameworks, model governance, and responsible AI practices - Experience with Kubernetes and infrastructure-as-code (for example: Terraform) - Experience with real-time or streaming inference use cases - Contributions to open-source machine learning ecosystems or peer-reviewed publications Benefits list from posting: - competitive total rewards package including base salary determined based on the role, experience, skill set and location - Those in eligible roles may receive commission-based pay and/or discretionary incentive compensation, paid in the form of cash and/or forfeitable equity, awarded in recognition of individual achievements and contributions. - comprehensive health care coverage - on-site health and wellness centers - a retirement savings plan - backup childcare - tuition reimbursement - mental health support - financial coaching - more (maybe we keep "and more"). We should not include the sentence about additional details about total compensation and benefits will be provided during the hiring process (filler). Also not include EEO statements. Now About the role paragraph(s): The intro includes: "Join a team applying modern artificial intelligence and machine learning to high-impact, high-scale payments workflows. You will work with large datasets and complex operational processes to deliver measurable outcomes. You will build production-grade solutions spanning natural language processing, document understanding, and LLM-enabled applications. You will collaborate closely with business and technology partners to take ideas from concept to deployment. You will help raise engineering standards and mentor others while shipping real solutions. As an Applied AI/ML - Vice President in the Wholesale Payments Operations team within Consumer Investment Banking Team, you will build and deliver enterprise AI/ML solutions that improve operational efficiency and decisioning. You will partner with senior stakeholders to frame problems, define success metrics, and plan roadmaps. You will design, implement, and deploy production services on Amazon Web Services (AWS) with strong engineering rigor. You will establish model governance, monitoring, and responsible AI practices in line with risk and control expectations. You will mentor engineers and lead reviews that improve quality, reliability, and delivery speed. Wholesale Payments supports global client payments across multiple methods, currencies, and geographies. The role focuses on building scalable AI/ML capabilities for operations use cases, including document processing and workflow automation. You will contribute to reusable platforms and patterns that enable teams to safely deploy and operate models in production." We need to preserve all that. We'll put in

maybe multiple paragraphs but can be one. Now produce HTML fragment. We must not include or . Just the fragment. Let's craft:

About the role

Join a team applying modern artificial intelligence and machine learning to high-impact, high-scale payments workflows. You will work with large datasets and complex operational processes to deliver measurable outcomes. You will build production-grade solutions spanning natural language processing, document understanding, and LLM-enabled applications. You will collaborate closely with business and technology partners to take ideas from concept to deployment. You will help raise engineering standards and mentor others while shipping real solutions. As an Applied AI/ML - Vice President in the Wholesale Payments Operations team within Consumer Investment Banking Team, you will build and deliver enterprise AI/ML solutions that improve operational efficiency and decisioning. You will partner with senior stakeholders to frame problems, define success metrics, and plan roadmaps. You will design, implement, and deploy production services on Amazon Web Services (AWS) with strong engineering rigor. You will establish model governance, monitoring, and responsible AI practices in line with risk and control expectations. You will mentor engineers and lead reviews that improve quality, reliability, and delivery speed. Wholesale Payments supports global client payments across multiple methods, currencies, and geographies. The role focuses on building scalable AI/ML capabilities for operations use cases, including document processing and workflow automation. You will contribute to reusable platforms and patterns that enable teams to safely deploy and operate models in production.

Responsibilities

• Partner with senior business stakeholders to frame problems, define success metrics, and align AI/ML roadmaps to priorities
• Lead architecture, design, and end-to-end delivery of enterprise AI/ML solutions for Wholesale Payments Operations
• Write clean, performant, production-quality code and set engineering standards across the team
• Champion modern software development life cycle (SDLC), continuous integration and continuous delivery (CI/CD), and DevOps practices
• Deploy and operate AI/ML services on AWS at scale
• Apply advanced techniques including data/text mining, document analysis, classification, optical character recognition (OCR), natural language processing (NLP), and LLM workflows (including retrieval-augmented generation and fine-tuning)
• Design and implement scalable, secure data pipelines to support model training and inference
• Define and enforce MLOps, model governance, monitoring, and responsible AI practices; represent the team in architecture and risk forums
• Evaluate model performance in production, including drift management and reproducibility
• Mentor engineers, conduct code and design reviews, and support recruiting and talent development

Required Qualifications

• Master's degree in Computer Science, Engineering, Mathematics, or a related quantitative field
• 6 years of professional software engineering experience delivering production systems
• 4 years of advanced Python development in production environments, including use of AI-assisted coding tools (e.g., GitHub