Software Engineer III - AWM Technology
Confirmed on the employer's own hiring board on Oct 24, 2026. First seen by Alion on Oct 12, 2026. Campbell Global scores A on the Alion truth index.
We have an exciting and rewarding opportunity for you to take your software engineering career to the next level.
As a Software Engineer III at JPMorganChase within the Asset & Wealth Management Technology organization, you serve as a seasoned member of an agile team to design and deliver trusted market-leading technology products in a secure, stable, and scalable way. You are responsible for carrying out critical technology solutions across multiple technical areas within various business functions in support of the firm’s business objectives.
As a Software Engineer III within Asset Management’s Sales Experience Platform (SEP) Team, you will play a critical role in designing, developing, and maintaining large-scale data integration solutions that support complex enterprise environments. You will leverage your hands-on expertise in production-grade Python, microservices, APIs, and AWS cloud technologies to deliver scalable, secure, and high-performance software solutions. This role offers the opportunity to work on cutting-edge financial technology projects that drive business value and innovation in asset management.
Job Responsibilities
Design, develop, and implement large-scale data integration solutions across complex enterprise systems to support asset management business needs.
Build and maintain production-grade Python applications with a focus on reliability, scalability, and performance.
Develop and consume microservices and APIs to enable seamless data flow and system interoperability.
Utilize AWS cloud services to architect, deploy, and manage cloud-native applications and infrastructure.
Collaborate with cross-functional teams including product owners, data architects, and business analysts to translate requirements into technical solutions.
Participate in code reviews, testing, and continuous integration/continuous deployment (CI/CD) pipelines to ensure high-quality software delivery.
Troubleshoot and resolve production issues, ensuring minimal downtime and optimal system performance.
Contribute to the design and evolution of data models to support efficient data storage, retrieval, and processing.
Engage in knowledge sharing and mentoring of junior engineers to foster a collaborative engineering culture.
Leverages enterprise-authorized AI coding assist tools within the work environment to improve code quality, delivery speed, and productivity across complex deliverables (e.g., code generation/refactoring, unit test creation, documentation), while validating outputs through peer review, automated testing, and secure coding standards; contributes learnings and reusable patterns to improve broader team effectiveness.
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.
Required qualifications, capabilities, and skills
Formal training or certification on software engineering concepts and 3+ years applied experience
Hands-on practical experience in system design, application development, testing, and operational stability
Experience in developing, debugging, and maintaining code in a large corporate environment with one or more modern programming languages (Python, TypeScript and/or Java) and database querying languages
Proven experience with large-scale data integrations in complex enterprise environments.
Expertise in designing, building, and consuming microservices and RESTful APIs.
Solid experience with AWS cloud services including but not limited to EC2, Lambda, S3, API Gateway, Glue and CloudFormation.
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.
Overall knowledge of the Software Development Life Cycle and best practices including version control, automated testing, and CI/CD.
Solid understanding of agile methodologies such as CI/CD, Application Resiliency, and Security
Preferred qualifications, capabilities, and skills
Experience integrating Salesforce platforms
Knowledge of data model design principles and experience implementing data models in relational or NoSQL databases.
Familiarity with containerization technologies such as Docker and orchestration tools like Kubernetes.
Proficiency in utilizing AI-powered coding tools, including VS Code integrated plugins such as DevGPT, Claude Code, and similar technologies, to enhance development productivity and code quality.
Understanding of financial services domain, particularly asset management workflows and data requirements.