Applied AI/ML Lead - Small Business

JPMorgan Chase; New York (NY); On-site; USD 180,000 - 240,000; Full time

Job summary

Chase Small Business Banking is seeking an Applied AI and Machine Learning Lead to set the technical direction for agentic AI products in Small Business Lending. You will lead design and production delivery of agent behavior across prompts, retrieval, tools, evaluation, routing, and memory, while owning the measurement loop to prove success.

We expect a strong record of leading AI/ML services from concept to deployment, with hands-on Python, data pipelines, and evaluation design.

Qualifications

• Advanced degree and 5+ years in CS or data science.
• Experience deploying production ML/AI solutions.
• Leadership of AI/ML services from ideation to deployment.
• Built evaluation and safety controls for nondeterministic systems.
• Experience with data pipelines, SQL, and diverse data.
• Strong statistical methods and experimental design.
• Clear communication to technical and business partners.
• Experience with RAG, vector databases and contextual retrieval.
• Experience fine-tuning foundation models and embeddings.
• Expertise with LangChain/LangGraph and related orchestration tools.
• Experience with tracing/observability tools like Arize Phoenix.

Responsibilities

• Set the technical direction for applied AI in Small Business Lending and define metrics and plan.
• Lead projects from discovery through production, managing priorities and risks.
• Guide agent design across prompts, context, retrieval, tools, evaluation, routing and memory.
• Define evaluation, safety, and guardrails for nondeterministic systems.
• Partner with Engineering on infrastructure, retrieval, tool APIs, telemetry, and release readiness.
• Mentor and guide junior team members.
• Cultivate stakeholder relationships and communicate with senior management.

Skills

Production ML/Gen AI

Team leadership

Data pipelines

Experiment design & A/B testing

Communication to tech/business

RAG & vector databases

LLM orchestration (LangChain/LangGraph

Observability & monitoring

Embeddings fine-tuning

Education

Master's or PhD in CS or data science

Tools

Python

LangChain

LangGraph

Arize Phoenix

Vector databases

Job description

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.