Applied AI/ML Lead - Small Business

Chase Small Business Banking is a world-class brand serving 7 million small business clients with a full suite of financial services, including lending, deposits, cards, and online solutions. As an Applied AI and Machine Learning Lead, you will set the technical direction for agentic AI products supporting Small Business Lending. You will lead the design and production delivery of agent behavior across prompts, retrieval, tools, evaluation, routing, and memory, while owning the measurement loop that demonstrates whether the system works. Partnering closely with Product and Engineering, you will define meaningful outcomes, guide a small team, and ensure the solutions are reliable, safe, cost-aware, and appropriate for a regulated financial environment. Job Responsibilities Set the technical direction for applied AI in Small Business Lending, translating ambiguous needs into a solution strategy, success measures, and delivery plan Lead projects from discovery through production, managing priorities, risks, dependencies, and post-launch results Guide agent design across prompts, context, retrieval, tools, evaluation, routing, and memory Define evaluation, safety, and guardrail approaches for non-deterministic systems Partner with Engineering on infrastructure, retrieval, tool APIs, telemetry, and release readiness Mentor and guide junior team members Cultivate stakeholder and partner relationships, communicate effectively to senior management Required Qualifications, Capabilities, and Skills Master's or PhD degree in computer science or a data science-related discipline, with 5+ years of industry experience Developed and deployed production machine learning or generative AI solutions using Python Experience leading AI/ML services from ideation to deployment, including design, code review, automated testing, CI, monitoring, and post-release improvement Built evaluations and safety controls for nondeterministic and agentic systems Worked with data pipelines, SQL, and structured and unstructured data Advanced knowledge of statistical methods, experimental design and A/B testing Communicated complex findings and recommendations clearly to technical and business partners Experience with RAG, vector databases, search, and structured or unstructured context Experience fine-tuning foundation models, training embeddings, or optimizing inference Expertise with LLM orchestration frameworks such as LangChain, LangGraph, or similar tools. Experience with tracing and observability tools such as Arize Phoenix.