AI ML Engineering Lead - Vice President - Wholesale Payments Operations

Job 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, Capabilities, And Skills

• 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
• Experience with 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
• Experience mentoring engineers and driving execution against multi-quarter roadmaps
• Strong communication skills, including translating business needs into technical deliverables for senior

Preferred Qualifications, Capabilities, And Skills

• 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