Join the Payments and Global Banking Technology team as an Executive Director and Senior Principal 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 robust, scalable systems. You will design and build production search, conversational AI, and agentic workflow systems powered by generative AI to solve real business problems in banking and financial services. You will work with a team of engineers, but this is not a people management role.

As an Executive Director-Applied AI/ML Lead in Payments and Global Banking Technology, you serve as the senior hands-on technical authority designing and building production search, conversational AI, and agentic workflow systems powered by generative AI. You lead through technical excellence—setting the bar, unblocking hard problems, and increasing team effectiveness through craft and judgment. You make architecture and implementation decisions across retrieval strategies, agent design, orchestration patterns, and system design. You focus on building robust, scalable systems that solve real business problems in banking and financial services.

You will collaborate closely with engineering, product, and business stakeholders to translate requirements into high-quality technical solutions, while raising engineering standards through design reviews, code reviews, and mentorship.

Job responsibilities:

• Architect, design, and personally implement production-grade search, chatbot, and agentic workflow systems, writing and reviewing code daily.
• Serve as the organization’s senior technical authority on large language model (LLM)-powered applications, owning decisions around retrieval strategies, agent design, orchestration patterns, and system architecture.
• Design and build scalable retrieval pipelines (hybrid search, re-ranking, chunking, and embedding strategies) and conversational AI systems with robust dialogue management and backend integration.
• Design and implement agentic workflows (tool use, multi-step planning, orchestration, error recovery, and human-in-the-loop patterns) for complex business processes.
• Solve hard systems challenges, including latency optimization, reliability at scale, observability, and evaluation of non-deterministic systems.
• Define technical standards, design patterns, and reference architectures for generative AI-powered applications across the organization.
• Elevate engineering quality through design reviews, code reviews, and technical mentorship.
• Partner with product and business stakeholders to translate requirements into technical designs.

Required qualifications, capabilities, and skills:

• 10+ years of hands-on software engineering experience building production systems at scale, with significant recent focus on search, conversational AI, or workflow automation.
• Expert-level proficiency in Python, distributed systems, application programming interface (API) design, and microservices architecture.
• Strong understanding of LLM capabilities and limitations, including prompt engineering, context management, and output parsing, and effective use of models as components without training or fine-tuning them.
• Deep experience with retrieval systems, conversational AI, or agentic architectures (tool calling, multi-step planning, orchestration, and failure handling), across more than one of these areas.
• Strong systems thinking, including designing for latency, throughput, cost efficiency, and graceful degradation in non-deterministic AI systems.
• Hands-on experience with Amazon Web Services (AWS) cloud services for production applications.
• Proven ability to elevate team output through technical leadership without relying on management hierarchy.
• Excellent technical communication skills across engineering and non-technical audiences.

Preferred qualifications, capabilities, and skills:

• Experience building evaluation and testing frameworks for LLM-powered applications.
• Familiarity with advanced retrieval-augmented generation (RAG) patterns, including query decomposition, multi-index routing, contextual compression, and knowledge graph integration.
• Experience with agent orchestration at scale, including parallel tool execution, stateful workflows, and cost/token management.
• Experience operating AI-powered systems in regulated environments, including model governance and explainability.