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As an AI/ML Software Engineer at JPMorgan Chase within the Infrastructure Platform community, you will serve as a seasoned member of an agile team, focusing on designing, developing, and delivering trusted, market-leading AI/ML technology products in a secure, stable, and scalable manner. You will play a key role in implementing critical AI/ML solutions across multiple technical areas, supporting various business functions to achieve the firm’s business objectives and drive innovation in artificial intelligence and machine learning.
Job responsibilities
- Designs, develops, and deploys advanced AI/ML models and algorithms, integrating them into production systems to solve complex business challenges.
- Executes software solutions, design, development, and technical troubleshooting with the ability to think beyond routine or conventional approaches to build innovative AI/ML solutions or break down technical problems.
- Creates secure and high-quality production code, maintaining and optimizing machine learning algorithms that run synchronously with appropriate systems.
- Produces architecture and design artifacts for complex AI/ML applications, ensuring design constraints are met by software code development.
- Gathers, analyzes, synthesizes, and develops visualizations and reporting from large, diverse data sets to continuously improve AI/ML software applications and systems.
- Proactively identifies hidden problems and patterns in data, using these insights to drive improvements to AI/ML model performance, coding hygiene, and system architecture.
- Collaborates with cross-functional teams to integrate AI/ML capabilities into business products and services, ensuring alignment with business goals.
- Contributes to software engineering and AI/ML communities of practice and events that explore new and emerging technologies.
Required qualifications, capabilities, and skills
- Formal training or certification on software engineering concepts and applied experience.
- Hands-on practical experience in system design, application development, testing, and operational stability, with a focus on AI/ML solutions.
- Proficient in coding in one or more languages, with strong experience in Python and Py Spark for AI/ML model development and data analytics.
- Experience in developing, debugging, and maintaining code in a large corporate environment with one or more modern programming languages and database querying languages.
- Overall knowledge of the Software Development Life Cycle, with experience deploying AI/ML models and pipelines.
- Experience in data engineering or related roles, with strong hands-on experience with AWS cloud services (S3, EMR, Lambda, Glue, etc.) for AI/ML workloads.
- Experience with machine learning frameworks and libraries (e.g., Tensor Flow, Py Torch, Scikit-learn) and deploying models in production environments.
- Solid understanding of agile methodologies such as CI/CD, Application Resiliency, and Security, especially as they relate to AI/ML systems.
- Demonstrated passion and enthusiasm for advancing AI/ML solutions and staying current with industry trends and best practices.
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About JPMorgan Chase

JPMorgan Chase
PublicJPMorgan Chase is a multinational investment bank and financial services company that provides banking, investment, and asset management services globally. It is one of the largest banks in the United States by assets and market capitalization.
300,000+
Employees
New York City
Headquarters
Reviews
4.2
10 reviews
Work Life Balance
4.2
Compensation
4.3
Culture
4.5
Career
4.4
Management
4.1
75%
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Pros
Good pay and benefits
Work-life balance
Career advancement opportunities
Cons
Heavy workload at times
Career advancement takes time
Pay could be better in some roles
Salary Ranges
47 data points
Junior/L3
Mid/L4
Senior/L5
Junior/L3 · Analyst
21 reports
$126,500
total / year
Base
$110,000
Stock
-
Bonus
-
$95,450
$155,250
Interview Experience
4 interviews
Difficulty
2.8
/ 5
Duration
14-28 weeks
Interview Process
1
Application Review
2
HireVue Video Interview
3
Technical/Behavioral Assessment
4
Final Interview Round
5
Offer Decision
Common Questions
Behavioral/STAR
Technical Knowledge
Past Experience
Culture Fit
Case Study
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