Are you looking for an exciting opportunity to join a dynamic and growing team in a fast paced and challenging area? This is a unique opportunity for you to work in our team to partner with the Business to provide a comprehensive view.
As an Wholesale Credit Risk Associate - Quantitative Research / AI-ML Engineer you will support the design, development, and operationalization of AI/ML/LLM/Gen AI solutions that deliver measurable business impact. Working under the guidance of senior engineers, you will help prepare and transform data, build and maintain scalable ETL pipelines in Python and Databricks, and contribute to fine‑tuning and evaluating generative models for domain specific tasks such as summarization and text generation. You will assist in developing and deploying microservices on cloud platforms using Docker and Kubernetes, and support real‑time monitoring to ensure model and system performance.
Job Responsibilities:
- Design, develop, and deploy state-of-the-art AI/ML/LLM/Gen AI solutions to meet business objectives.
- Develop and maintain automated pipelines for data, ensuring scalability, reliability, and efficiency.
- Implement optimization strategies to fine-tune generative models for domain specific use cases, ensuring high-quality outputs in summarization and text generation.
- Conduct thorough evaluations of generative models iterate on model architectures and implement improvements to enhance overall performance in AI/ML applications.
- Implement monitoring mechanisms to track AI/ML solutions performance in real-time and ensure model reliability.
- Communicate AI/ML/LLM/GenAI capabilities and results to both technical and non-technical audiences.
- Stay informed about the latest trends and advancements in the latest AI/ML/LLM/GenAI research, implement cutting-edge techniques, and leverage external APIs for enhanced functionality.
Required Qualifications, Skills and Capabilities:
- Master’s with at least 2+ years’ experience in Computer Science, Data Science, Machine Learning, or a related field.
- Proficiency in Python for model development, experimentation, and integration with the LLM APIs.
- Ability to identify and address AI/ML/LLM/GenAI challenges, implement optimizations, and fine-tune models for optimal performance in AI/ML applications.
- Experience with cloud platforms (preferably AWS), containerization technologies (Docker and Kubernetes), and microservices design, implementation, and performance optimization.
- Proficient in building AI Agents (e.g., LangChain, LangGraph), integration of tools, and RAG-based solutions, Knowledge Graphs (e.g., neo4J).
- Experience developing end-to-end data workflows and ETL pipelines using Python and Databricks.
- Strong understanding of data modeling, normalization, and database design principles.
- Experience developing, debugging, and maintaining code in a large corporate environment using one or more modern programming languages and database querying languages.
- Ability to gather, analyze, and synthesize large, diverse data sets and to develop visualizations and reports in support of continuous improvement of applications and systems.
- Strong collaboration skills to work effectively with cross-functional teams, communicate complex concepts, and contribute to interdisciplinary projects.
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
- Master's degree in Computer Science, Data Science, Machine Learning, or a related field
- At least 2 years of relevant experience
- Proficiency in Python for model development, experimentation, and LLM API integration
- Experience addressing AI, ML, LLM, and generative AI challenges, including model optimization and fine-tuning
- Experience with cloud platforms, preferably AWS
- Experience with Docker and Kubernetes
- Experience designing, implementing, and optimizing microservices
- Experience building AI agents using tools such as LangChain or LangGraph
- Experience with tool integration and retrieval-augmented generation solutions
- Experience with knowledge graphs, such as Neo4j
- Experience developing end-to-end data workflows and ETL pipelines using Python and Databricks
- Strong understanding of data modeling, normalization, and database design
- Experience developing, debugging, and maintaining code in a large corporate environment
- Experience with modern programming languages and database querying languages
- Ability to analyze diverse datasets and develop visualizations and reports
- Strong collaboration and communication skills
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.
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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.
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