Senior software engineer responsible for designing, developing, and operating cloud-native, microservices-based applications. Build secure, production-quality code, design architectures, implement CI/CD and observability, use event-driven data platforms, and develop agentic LLM-driven coding pipelines while ensuring responsible AI and operational excellence.
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 Chase Auto Finance, 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.
Job responsibilities
- Executes software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or break down technical problems
- Creates secure and high-quality production code and maintains algorithms that run synchronously with appropriate systems
- Produces architecture and design artifacts for complex applications while being accountable for ensuring design constraints are met by software code development
- Gathers, analyzes, synthesizes, and develops visualizations and reporting from large, diverse data sets in service of continuous improvement of software applications and systems
- Proactively identifies hidden problems and patterns in data and uses these insights to drive improvements to coding hygiene and system architecture
- 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.
- Contributes to software engineering communities of practice and events that explore new and emerging technologies
- Adds to team culture of diversity, opportunity, inclusion, and respect
- Develop and govern agentic coding pipelines leveraging LLMs, retrieval, and automated testing.
Required qualifications, capabilities, and skills
- 10+ years of applied software engineering experience with formal training/certification (or equivalent), delivering solutions end-to-end across design, development, testing, deployment, and production operations.
- Strong Java engineering background building cloud-native, microservices-based applications using Spring Boot/Spring Cloud.
- Proven experience with event-driven architectures and data platforms: Kafka, SQL, RDBMS, and NoSQL databases (e.g., Cassandra).
- Expertise in CI/CD and automation using modern toolchains (e.g., Jenkins, Bitbucket, JIRA) and Agile delivery practices.
- Strong focus on operational excellence: observability/monitoring and incident readiness using Splunk, Dynatrace, and Datadog.
- Deep understanding of application resiliency, security, chaos engineering, and performance testing (e.g., BlazeMeter).
- Domain knowledge of auto financial services and the technology ecosystems supporting lending/servicing and related integrations.
- Ability to design and operate agent-based LLM workflows for software delivery (plan/write/test/iterate) with tool/function calling, guardrails, and human-in-the-loop controls.
- 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.
Preferred qualifications, capabilities, and skills
- Familiarity with modern front-end and back-end technologies
- Exposure to cloud technologies
- Exposure to AI tools
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