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, and cost/token management.
• Experience working in regulated environments with awareness of model governance and explainability requirements.