Vice President-Applied AI/ML Lead

Join the Payments and Global Banking Technology team as a Vice President and Engineer for Applied AI/ML. This is a hands-on, code-every-day role focused on application-layer engineering—using large language models as components within scalable systems that solve real business problems in banking and financial services. You will work within a team of engineers under the guidance of senior technical leaders, delivering high-quality features and systems end-to-end while developing your expertise in generative AI application development.
As a Vice President – Applied AI/ML Lead in Payments and Global Banking Technology, you design and build production search, conversational AI, and agentic workflow systems powered by generative AI. You deliver high-quality features and systems end-to-end within established architectural frameworks. You collaborate with senior technical leaders and cross-functional partners to translate requirements into scalable implementations. You contribute to engineering quality through strong implementation practices and peer review.
This role emphasizes practical application-layer engineering, including retrieval pipelines, conversational experiences, and workflow automation patterns, with a focus on reliability, performance, and maintainability in non-deterministic AI systems.
Job responsibilities:
Implement and iterate on production search, chatbot, and agentic workflow features, writing and reviewing code daily.
Build retrieval pipelines (hybrid search, re-ranking, chunking, and embedding strategies) and integrate them into conversational AI systems with dialogue management and backend services.
Implement components of agentic workflows (tool use, orchestration, error recovery, and human-in-the-loop patterns) within established architectural frameworks.
Troubleshoot and resolve systems issues related to latency, reliability, and observability in non-deterministic AI systems.
Follow and contribute to team technical standards, design patterns, and best practices for generative AI-powered applications.
Participate in design reviews and code reviews to maintain engineering quality.
Collaborate with product and business stakeholders to understand requirements and deliver technical solutions.
Required qualifications, capabilities, and skills:
6+ years of hands-on software engineering experience building production systems at scale, with exposure to search, conversational AI, or workflow automation.
Proficiency in Python, application programming interface (API) design, and microservices architecture.
Working understanding of large language model (LLM) capabilities and limitations, including prompt engineering, context management, and output parsing, and practical use of models as components.
Experience with at least one of the following: retrieval systems, conversational AI, or agentic architectures (tool calling, planning, and orchestration).
Awareness of systems-level concerns, including latency, throughput, cost efficiency, and graceful degradation.
Experience with Amazon Web Services (AWS) cloud services for production applications.
Strong communication and collaboration skills across technical and non-technical audiences.
Preferred qualifications, capabilities, and skills:
Experience building evaluation and testing frameworks for LLM-powered applications.
Familiarity with retrieval-augmented generation (RAG) patterns, including query decomposition, multi-index routing, and contextual compression.
Exposure to agent orchestration, including parallel tool execution, stateful workflows,