Job description

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 Carry out critical tech solutions across multiple technical areas as an integral part of an agile team

Requirements

Must-haves

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

Nice-to-haves

Preferred qualifications, capabilities, and skills:

Application prep

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Keywords to mirror

agilemlaiawsrestsqlscrummachine learning

Platform: oracle-cloud
Effort: medium