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Applied Artificial Intelligence Machine Learning Lead - Vice President

JPMorgan Chase · jpmorganchase.com
WilmingtonExecutive7+ yrs
Apply on JPMorgan Chase’s site

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The job in short

AI and machine learning leadership.

PythonSQLNoSQLPostgresRedisAWSAzureMachine learningGenAILLMLLMs

Details

Found
17 d ago · posted 2026-09-22
Location as listed
Wilmington, DE, United States
Role
Executive / Director
Source
The company’s own careers site

Full description

JPMC is hiring to join the growing Asset and Wealth Management AI Engineering team. We are executing like a startup and building next-generation technology that combines JPMC unique data and full-service advantage to develop high impact AI applications and platforms in the financial services industry. We are looking for a hands-on AI Engineering lead and domain expert who is excited about the opportunity to build cutting edge business solutions powered by AI. As a AI/ML Lead Engineer Vice President in our global AI team you will play a lead role as a seasoned member of our global AI team. Your responsibilities will entail hands-on development of high-impact business solutions through data analysis, developing cutting-edge ML and LLM models, and deploying these models to production environments on AWS or Azure. You’ll combine your years of proven development expertise with a never-ending quest to create innovative technology through solid engineering practices. Your passion and experience in one or more technology domains will help solve complex business problems to serve our Private Bank clients. As a constant learner and early adopter, you’re already embracing leading-edge technologies and methodologies; your example encourages others to follow suit. Job Responsibilities • Collaborate with firmwide AI/ML teams, Business and Product Partners, peers in geographically dispersed teams, and colleagues across JPMorgan AWM’s lines of business and functions to drive alignment, accelerate adoption of common AI capabilities, and deliver impactful solutions • Hands-on architecture and implementation of lighthouse ML and LLM-powered solutions • Design and implement highly scalable and reliable data processing pipelines and deploy model inference services • Experiment, develop, and productionize high-quality machine learning models, services, and platforms to make a huge technology and business impact • Deploy solutions into public cloud infrastructure Required Qualifications, Capabilities, and Skills • MS in Computer Science, Statistics, Mathematics, or Machine Learning and minimum 7 years of development experience, with at least 4 years working on AI solutions. • Experience in using LLMs (OpenAI, Anthropic, or other models) to solve business problems, including full workflow toolset such as tracing, evaluations, and guardrails; • Must have strong programming skills in Python and deep knowledge in Data Structures, Algorithms, Machine Learning, Data Mining, Information Retrieval, and Statistics • Demonstrated ability to perform independent end-to-end solution design, with attention on modern architecture, scalability and security • Knowledge of data management, including data model design, as well as real-time data processing on both SQL (such as Postgres) and NoSQL stores (such as OpenSearch and Redis) • Proven leadership capacity, including new AI/ML idea generation and GenAI-based solutions and excellent communication skills and ability to communicate with senior technical and business partners Preferred Qualifications, Capabilities, and Skills • Experience working in the financial domain with a large financial institution in Asset and Wealth Management, Brokerage, or Investment Banking will be an added advantage • Understanding of LLM fine-tuning and small language model inference is a plus • Ability to develop full-stack products, utilizing modern JavaScript and TypeScript frameworks, such as Next.js and Svelte • Knowledge of other high-performance languages, such as Go or Rust • Expert knowledge of one of the cloud computing platforms preferred: Amazon Web Services (AWS), Azure, Kubernetes

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