Join a world-class Applied AI/ML organization at JPMorganChase and help shape how teams across the firm use data science, machine learning, and Generative AI to solve real business problems.
As a Senior Associate in Applied Artificial Intelligence and Machine Learning within Shared Services, you design and deploy predictive models, advanced analytics, and large language model agentic solutions that orchestrate tools and workflows inside business processes. You wil build reusable services that teams can adopt to improve risk assessment support, legal and regulatory change mapping, control design and testing, sustainable monitoring, issue tracking, and governance reporting.
Job Responsibilities
- Design, develop, and deploy predictive machine learning, advanced analytics, and generative AI solutions for complex business problems
- Build and integrate agentic workflows into end-to-end business processes, including retrieval-augmented generation, tool or function calling, routing, and structured outputs
- Prototype AI-enabled approaches quickly and harden successful prototypes into reusable, production-ready services with measurable outcomes
- Own end-to-end model delivery, including dataset preparation, feature engineering, training, validation, evaluation, deployment, and iteration
- Design, deploy, and operate production pipelines and services (batch and real-time), including monitoring, retraining strategies, and reliability and cost improvements
- Partner with product, engineering, and risk and controls stakeholders to define requirements, align on success metrics, and drive adoption
- Apply responsible AI and governance-aligned practices across the model and agent lifecycle, including evaluation, guardrails, and documentation
- Contribute reusable patterns, templates, and libraries that accelerate delivery across teams
Required qualifications, capabilities, and skills
- Bachelor’s degree in data science, computer science, statistics, mathematics, or a related technical field (or equivalent practical experience)
- Three years of experience delivering end-to-end AI or machine learning solutions from prototype to production (or production-like) deployment
- Strong Python proficiency for data analysis, modeling, and production implementation
- Experience building, evaluating, and deploying predictive models (for example, classification, regression, or natural language processing) using common libraries (for example, PyTorch, TensorFlow, or scikit-learn)
- Experience building and deploying large language model workflows that include retrieval-augmented generation and tool or function calling
- Experience defining and using an evaluation approach for large language model solutions (for example, test sets, regression tests, or structured human review)
- Experience operating production pipelines or services, including versioning, continuous delivery practices, monitoring and alerting, and incident hygiene
- Working knowledge of cloud and or containerized environments (for example, Amazon Web Services, Microsoft Azure, Google Cloud Platform, or Kubernetes)
- Strong communication skills, including the ability to translate business problems into measurable technical outcomes and explain results to diverse audiences
Preferred qualifications, capabilities, and skills
- Master’s degree or doctorate in a quantitative field
- Publications, patents, or meaningful open-source contributions related to machine learning or generative AI
- Experience scaling agentic systems across multiple use cases, including mature evaluation practices and quality dashboards
- Experience implementing guardrail patterns and operating controls for generative AI features in production
- Experience with large-scale data processing and cloud data services, and exposure to performance optimization for model serving
- Experience in financial services or other regulated industries, including comfort operating within governance and change management expectations
- Experience with specialized domains such as search and ranking, recommender systems, graph machine learning, or knowledge graphs
About Us
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.
Skills Required
- Bachelor's degree in data science, computer science, statistics, mathematics, or related technical field (or equivalent experience)
- Three years of experience delivering end-to-end AI or machine learning solutions from prototype to production (or production-like) deployment
- Strong Python proficiency for data analysis, modeling, and production implementation
- Experience building, evaluating, and deploying predictive models (classification, regression, NLP) using libraries such as PyTorch, TensorFlow, or scikit-learn
- Experience building and deploying large language model workflows, including retrieval-augmented generation and tool/function calling
- Experience defining and using evaluation approaches for LLM solutions (test sets, regression tests, structured human review)
- Experience operating production pipelines/services, including versioning, continuous delivery practices, monitoring, alerting, and incident hygiene
- Working knowledge of cloud and/or containerized environments (AWS, Azure, GCP, or Kubernetes)
- Strong communication skills to translate business problems into measurable technical outcomes
- Master's degree or doctorate in a quantitative field
- Publications, patents, or meaningful open-source contributions related to ML or generative AI
- Experience scaling agentic systems across multiple use cases with mature evaluation practices and quality dashboards
- Experience implementing guardrail patterns and operating controls for generative AI in production
- Experience with large-scale data processing, cloud data services, and performance optimization for model serving
- Experience in financial services or other regulated industries and operating within governance/change management
- Experience with specialized domains such as search and ranking, recommender systems, graph ML, or knowledge graphs
JPMorganChase Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about JPMorganChase and has not been reviewed or approved by JPMorganChase.
-
Healthcare Strength — Medical, dental, vision, and mental health coverage are comprehensive, with on-site clinics, preventive care, and specialized supports such as maternity nurse guidance and fertility treatments. Wellness activities can help offset copays and out-of-pocket costs, reinforcing the perceived strength of health benefits.
-
Retirement Support — A 401(k) with dollar-for-dollar matching and additional automatic pay credits reflect strong employer-backed retirement savings. An employee stock purchase plan and related financial programs further bolster long-term financial support.
-
Leave & Time Off Breadth — Paid time off, sick time, holidays, and generous parental leave are provided alongside family medical leave and adoption/fertility assistance. Additional programs like caregiver support and volunteer time off expand the breadth of time-away options.
JPMorganChase Insights
What We Do
JPMorgan Chase & Co. (NYSE: JPM) is a leading global financial services firm with assets of $3.7 trillion and operations worldwide. The firm is a leader in investment banking, financial services for consumers and small businesses, commercial banking, financial transaction processing, and asset management. A component of the Dow Jones Industrial Average, JPMorgan Chase & Co. serves millions of consumers in the United States and many of the world’s most prominent corporate, institutional and government clients under its J.P. Morgan and Chase brands. Technology fuels every aspect of our company and is at the heart of everything we do. With over 50,000 technologists globally and an annual tech spend of $12 billion, we are dedicated to improving the design, analytics, development, coding, testing and application programming that goes into creating high quality software and new products. Learn more about technology at our firm, explore resources from our Distinguished Engineers, AI & ML researchers, and other experts; access the latest episode of our TechTrends podcast, and more at www.jpmorgan.com/technology. Information about JPMorgan Chase & Co. is available at www.jpmorganchase.com. ©2023 JPMorgan Chase & Co. All rights reserved. JPMorgan Chase is an Equal Opportunity Employer, including Disability/Veterans.
Why Work With Us
Our technologists work on a diverse range of solutions that include strategic technology initiatives, big data, mobile, electronic payments, machine learning, cybersecurity, enterprise cloud development, and other state-of-the-art technologies.
Gallery
