About this role
JPMorganChase, one of the oldest financial institutions, offers innovative financial solutions to millions of consumers, small businesses and many of the world’s most prominent corporate, institutional and government clients under the J.P. Morgan and Chase brands. Our history spans over 200 years and today we are a leader in investment banking, consumer and small business banking, commercial banking, financial transaction processing and asset management.
We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants’ and employees’ religious practices and beliefs, as well as mental health or physical disability needs. Visit our FAQs for more information about requesting an accommodation.
Shape how modern payments teams find information, serve clients, and automate work using advanced language-model solutions. Join a high-impact engineering team where you will build production services that combine search, conversation, and workflow automation at enterprise scale. Grow your career through exposure to modern cloud platforms, disciplined engineering practices, and meaningful collaboration with product and business partners. As a Applied AI ML lead at JPMorgan Chase within the Payments and Global Banking Technology organization, you will design and build production search, conversational assistants, and automated workflow systems powered by generative artificial intelligence. You will deliver features and systems end-to-end, from technical design through implementation, testing, and production support, with a focus on reliability and performance. You will build retrieval pipelines and integrate them into conversational experiences with dialogue management and backend services. You will implement components of automated workflows, including tool use, orchestration, error recovery, and human-in-the-loop patterns, within established architectural frameworks. You will work within a team of engineers under the guidance of senior technical leaders while developing deeper expertise in language-model application development. 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, 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 GenAI-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, API design, and microservices architecture. • Working understanding of LLM capabilities and limitations—prompt engineering, context management, output parsing—and practical use of models as components. • Experience with at least one of: retrieval systems, conversational AI, or agentic architectures (tool calling, planning, orchestration). • Awareness of systems-level concerns—latency, throughput, cost efficiency, and graceful degradation. • Experience with 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 RAG patterns (query decomposition, multi-index routing, contextual compression). • Exposure to agent orchestration—parallel tool execution, stateful workflows, and cost/token management. • Experience working in regulated environments with awareness of model governance and explainability requirements.