Join a world-class Applied AI/ML organization at JPMorgan Chase and help shape how teams across the firm use data science, machine learning, and Generative AI to solve real business problems. In this shared services role, you’ll support Consumer & Community Banking Control Management Shared Services by delivering horizontal capabilities that strengthen how Control Managers operate day-to-day across Consumer & Community Banking businesses and functions (e.g., Auto, Home Lending, Credit Card, Consumer Banking, Business Banking, Operations, Branch Review, and ICB), spanning core activities like ongoing risk monitoring, process and regulatory understanding, metric/breach review, and building a holistic view of risks, controls, issues, action plans, applications, and intelligent automation in the control environment.
As a Senior Associate in Applied AI/ML (Shared Services), you will design and deploy predictive ML, advanced analytics, and GenAI/LLM agentic solutions—systems that orchestrate tools, workflows, and large language models within business processes—to create reusable services that scale across the Control Management lifecycle: maintaining risk assessment structures and tagging, supporting legal/regulatory change and obligation mapping, improving risk assessment and MRI alignment, enabling control design/testing and sustainable monitoring, accelerating issue identification/root-cause/action-plan tracking and validation, and strengthening governance, committees, scorecards, and reporting.
Job Responsibilities
- Design, develop, and deploy predictive ML, advanced analytics, GenAI/LLM, and agentic AI solutions for complex business problems in shared services.
- Build and integrate agentic workflows (tool use, RAG, routing/planning, structured outputs, evals/guardrails) into end-to-end business processes to deliver context-aware insights and automation.
- Prototype AI-enabled approaches quickly, then harden successful prototypes into reusable, production-ready services with measurable outcomes.
- Own end-to-end model delivery: dataset manipulation/feature engineering, training, validation, evaluation, deployment, and iteration.
- Design, deploy, and operate production ML pipelines and services (batch/real-time), including logging/metrics, monitoring, retraining/refresh strategies, and reliability/cost/latency improvements.
- Partner with product, engineering, and risk/controls stakeholders to define requirements, align on success metrics, and drive adoption.
- Apply responsible AI, governance, and compliance-aligned practices throughout the model and agent lifecycle; share best practices and contribute reusable templates/libraries.
Required Qualifications, Capabilities, and Skills
- Bachelor’s degree in data science, computer science, statistics, mathematics, or a related technical field (or equivalent practical experience).
- 3+ years experience or demonstrated ability to set up and deploy AI/ML solutions end-to-end (prototype → production or production-like), shown through prior roles, internships, research, or substantial projects.
- Strong Python proficiency for data analysis, modeling, and production-grade implementation; solid dataset manipulation and feature engineering skills.
- Hands-on experience building, evaluating, and deploying predictive models and analytics solutions (e.g., classification/regression, NLP) using common ML/deep learning libraries (e.g., PyTorch, TensorFlow, scikit-learn).
- Required agentic AI experience: built and deployed LLM-enabled agentic workflow (e.g., RAG + tool/function calling, routing/planning, structured outputs) with an evaluation approach (test set, regression tests, human review, or similar).
- Experience designing, deploying, and operating production ML/LLM pipelines or services, including basic MLOps practices (versioning, CI/CD for ML, monitoring/alerting, incident hygiene).
- Working knowledge of modern deployment environments: cloud (AWS/Azure/GCP) and/or containerized/distributed compute (e.g., Kubernetes).
- Strong communication and stakeholder partnership skills; ability to translate business problems into measurable technical outcomes and explain results to diverse audiences.
Preferred Qualifications
- Advanced education & thought leadership: Master’s or PhD in a quantitative field; publications, patents, or meaningful open-source contributions in ML/GenAI.
- Advanced agentic/GenAI maturity: scaled agentic systems beyond a single use case; strong LLM evaluation discipline (golden sets, automated regression, quality dashboards) and guardrail patterns.
- Scale/performance & data ecosystems: GPU/inference optimization (e.g., Triton, profiling), big data processing and cloud data services; exposure to RL or other advanced ML methods.
- Specialized ML domains & regulated environments: search/ranking, recommenders, graph ML/knowledge graphs; experience in financial services or other regulated industries and comfort operating within governance expectations—especially for regulatory/change management workflows.
What You’ll Build in Shared Services
- Reusable agent frameworks and patterns (routing, tool-use, workflow orchestration, safety controls) that multiple teams can adopt.
- LLM-powered capabilities embedded in business processes (summarization, classification, decision support, workflow automation) with measurable quality and risk controls.
- Deployed models supporting regulatory and change management (e.g., obligation/change classification and tagging, QA/routing, impact triage, and audit-ready decision support) integrated into workflows with monitoring and governance.
- Evaluation and monitoring foundations (golden sets, automated regression tests, drift/quality dashboards) that standardize how AI is operated at scale.
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 a related technical field, or equivalent practical experience
- 3 or more years of experience or demonstrated ability to deploy AI/ML solutions end-to-end from prototype to production or production-like environments
- Strong Python proficiency for data analysis, modeling, and production-grade implementation
- Strong dataset manipulation and feature engineering skills
- Hands-on experience building, evaluating, and deploying predictive models and analytics solutions such as classification, regression, or NLP
- Experience using common machine learning or deep learning libraries such as PyTorch, TensorFlow, or scikit-learn
- Experience building and deploying an LLM-enabled agentic workflow using methods such as RAG, tool or function calling, routing, planning, or structured outputs
- Experience applying an evaluation approach such as test sets, regression tests, human review, or similar
- Experience designing, deploying, and operating production ML or LLM pipelines and services
- Basic MLOps experience, including versioning, CI/CD for ML, monitoring, alerting, and incident hygiene
- Working knowledge of cloud environments such as AWS, Azure, or GCP and/or containerized or distributed compute such as Kubernetes
- Strong communication and stakeholder partnership skills, including translating business problems into measurable technical outcomes
- Master's or PhD in a quantitative field
- Publications, patents, or meaningful open-source contributions in machine learning or Generative AI
- Experience scaling agentic systems beyond a single use case
- Advanced LLM evaluation practices, including golden sets, automated regression, or quality dashboards
- GPU or inference optimization experience, including Triton or profiling
- Big data processing and cloud data services experience
- Exposure to reinforcement learning or other advanced machine learning methods
- Experience with search or ranking, recommenders, graph machine learning, or knowledge graphs
- Experience in financial services or other regulated industries
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.
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Healthcare Strength — Medical, dental, vision, and mental-health coverage are broad, with wellness incentives, on-site or virtual care, and an EAP offering coaching and counseling. Plan materials emphasize accessible options, including multiple medical choices and tools to manage costs.
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Parental & Family Support — Paid parental leave extends up to 16 weeks for all parents, supplemented by paid Critical Caregiver Leave. Family resources include backup childcare via Bright Horizons, lactation support and milk-shipping, family-building assistance, and even a free five-month SNOO rental for newborns.
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Retirement Support — Retirement programs include a 401(k) with an annual company match and automatic pay credits for most employees, with a legacy pension available to earlier hires. An Employee Stock Purchase Plan at a 5% discount further supports long-term savings.
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
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