JP Morgan Chase

Runs a consumer bank and an investment bank

Lead Software Engineer

Applied Machine LearningConfirmed live in the last 24 hours

Full-timeNo salary listedIn-person

About the Job

Requirements

Formal training or certification in software engineering concepts and at least 5 years of applied experience.
Hands-on experience with enterprise-authorized AI-assisted software development tools for coding, test creation, troubleshooting, or documentation, including the ability to critically evaluate, validate, and refine AI-generated outputs for correctness, performance, and security.
Understanding of responsible AI use in engineering workflows, including data sensitivity, secure handling of inputs and outputs, and adherence to resiliency and security expectations; ability to guide peers on safe and effective use within team practices.
Hands-on server-side development experience, including Web Services, XML, SOAP, REST, and JMS.
Experience building high-volume, low-latency, high-throughput transactional systems.
Experience building microservices using Spring Boot, Spring Core, MVC, Web Services, and Messaging.
Experience with AWS services including S3, DynamoDB, ECS, EKS, RDS, Lambda, and ALB/NLB.
Experience with pair-programming agents such as GitHub Copilot to accelerate prototyping.
Strong knowledge of relational database management systems, schema design, SQL, query optimization, indexing, joins, and JDBC.
Agile development experience with Scrum or similar methodologies.
Problem-solving skills and the ability to think critically and creatively.

Responsibilities

Design, build, and maintain platform components supporting the end-to-end machine learning model lifecycle, from development and training through deployment and monitoring.
Apply software development lifecycle toolchain knowledge, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized from automation.
Use enterprise-authorized AI coding-assistance tools to improve code quality, delivery speed, and productivity through tasks such as code generation and refactoring, unit test creation, and documentation; validate outputs through peer review, automated testing, and secure coding standards.
Build and manage cloud-based infrastructure on AWS, including Databricks, EMR, ECS, and S3, to support model training and serving workloads.
Automate model deployment, testing, and release processes within the software development lifecycle and MLOps toolchain.
Support the Platform team in developing and building automated solutions.
Monitor platform health and model-serving infrastructure, and identify and resolve performance and stability issues.
Apply AI-assisted development tools and best practices to improve code quality and delivery speed.
Contribute to a team culture of diversity, inclusion, and continuous improvement.

Desired Qualifications

Experience managing projects end to end, from requirements scoping to successful launch.
Experience in MLOps and building model-serving applications.
Experience with artificial intelligence and machine learning tools and frameworks in development.
Bachelor's degree in computer science, engineering, mathematics, or a related quantitative field and at least 10 years of applied experience, or a master's degree and at least 5 years of applied experience.
Completed AWS Developer or Solutions Architect certification.

We have an opportunity to impact your career and provide an adventure where you can push the limits of what's possible.

As a Lead Software Engineer at JPMorganChase within the Corporate Sector Technology, you are an integral part of an agile team that works to enhance, build, and deliver trusted market-leading technology products in a secure, stable, and scalable way. As a core technical contributor, you are responsible for conducting critical technology solutions across multiple technical areas within various business functions in support of the firm’s business objectives.

Job Responsibilities:

Design, build, and maintain platform components that support end-to-end ML model lifecycle — from development and training to deployment and monitoring
Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation
Leverages enterprise-authorized AI coding assist tools within the work environment to improve code quality, delivery speed, and productivity (e.g., code generation/refactoring, unit test creation, documentation), while validating outputs through peer review, automated testing, and secure coding standards.
Build and manage cloud-based infrastructure on AWS (including Databricks, EMR, ECS, and S3) to support model training and serving workloads
Automate model deployment, testing, and release processes within the SDLC/MLOps toolchain
Support Platform team in developing/building automated solutions
Monitor platform health and model serving infrastructure; identify and resolve performance and stability issues
Apply AI-assisted development tools and best practices to improve code quality and delivery speed
Contribute to a team culture of diversity, inclusion, and continuous improvement

Required qualifications, capabilities, and skills:

Formal training or certification on software engineering concepts and 5+ years applied experience
Hands-on experience using enterprise-authorized AI-assisted software development tools within the work environment (e.g., for coding, test creation, troubleshooting, or documentation) with demonstrated ability to critically evaluate, validate, and refine AI-generated outputs for correctness, performance, and security.
Understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; ability to guide peers on safe and effective usage within team practices.
Hands-on server-side development experience, including Web Services, XML, SOAP, REST, and JMS.
Experience in building high-volume, low-latency, high-throughput transactional systems.
Experience in building microservices using Spring Boot, Spring Core, MVC, Web Services and Messaging
Experience with AWS services - S3, DynamoDB, ECS, EKS, RDS, Lambda, and ALB/NLB.
Experienced with pair programming agents such as GitHub Copilot to accelerate prototyping.
Strong knowledge of RDBMS, schema design, SQL, query optimization, indexing, joins, and JDBC.
Agile Development experience with SCRUM or similar methodologies
Excellent problem-solving skills and the ability to think critically and creatively

Preferred qualifications, capabilities, and skills:

Experience managing projects end-to-end, from requirements scoping to successful launch.
Experience in MLOps and building model serving applications
Experience with artificial intelligence and machine learning tools and framework in development.
Bachelor’s degree in computer science, Engineering, Mathematics, or a related quantitative field with 10+ years of applied experience, or Master’s degree with 5+ years of applied experience.
Completed AWS Developer or Solution Architect Certification

About the Company

JP Morgan Chase

Runs a consumer bank and an investment bank

JPMorgan Chase runs the largest bank in the United States, which serves households and small businesses as Chase and large companies, governments, and investors as J.P. Morgan. During the 2008 financial crisis it bought the failing investment bank Bear Stearns and lender Washington Mutual, and in 2023 it took over First Republic Bank after regulators seized it. Chase takes deposits and earns interest on credit cards, mortgages, and car loans, while the investment side earns fees for advising on mergers, raising money for companies, trading stocks and bonds, and managing wealth. Formed in a 2000 merger, JPMorgan Chase traces its roots to 1799 and is based in New York City.

Company size: 10,001+
Company stage: IPO
Headquarters: New York City, New York
Founded: 1799

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