at J.P. Morgan
Bulge Bracket Investment BanksPosted 15 days ago
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**Lead Software Engineer** at JPMorganChase designs, builds, and maintains ML platform components, enhancing AI-assisted software development. Key responsibilities include cloud infrastructure management on AWS, automating model deployment, driving performance improvements, and fostering team culture. Ideal candidate possesses 5+ years of software engineering experience, AI-assisted development expertise, hands-on server-side development, microservices, and AWS services proficiency. Acute problem-solving skills and agile methodologies experience are crucial for this senior-level role.
- Compensation
- Not specified
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- Country
- United States
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Full Job Description
Location: Wilmington, DE, United States
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 firms 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
- 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
- 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.
- Bachelors degree in computer science, Engineering, Mathematics, or a related quantitative field with 10+ years of applied experience, or Masters degree with 5+ years of applied experience.
- Completed AWS Developer or Solution Architect Certification
