Software Engineer III

JPMorganChase | Asset and Wealth Management | Infrastructure Team 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 and Wealth Management Infrastructure team, 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 creative problem-solving to optimize system reliability, scalability, and performance. Creates secure and high-quality production code and maintains services/algorithms that integrate reliably with dependent systems and platforms. 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 CI/CD and enterprise-authorized AI-assisted development/automation) to reduce manual intervention and increase operational efficiency across deployment, monitoring, and operations. Produces architecture and design artifacts for complex applications including infrastructure, configuration, and network as code; accountable for ensuring design constraints and non-functional requirements are met in implementation. Gathers, analyzes, synthesizes, and develops visualizations and reporting from large, diverse data sets to drive continuous improvement of software applications and system operations. Proactively identifies hidden problems and patterns in production issues and data and uses these insights to improve coding hygiene, system architecture, and operational stability. Follows best practices in SRE, DevOps, and observability to monitor platform health, proactively address risks, and troubleshoot complex production incidents. Analyzes large volumes of structured, unstructured, and telemetry data to uncover trends/anomalies and translate findings into actionable reliability and performance improvements. Required Qualifications, Capabilities, and Skills Formal training or certification on software engineering concepts and 3+ years of applied experience. Experience in SRE/DevOps/Platform Engineering environments with demonstrated ownership of reliability, performance, and incident response. Experience with AWS services such as ECS, VPC, S3, IAM, Lambda, Aurora Postgres, Route53, ELB, API Gateway (and related cloud-native patterns). Strong understanding of automation and Infrastructure as Code (e.g., Terraform), including configuration and network as code for applications/platforms. Experience leading teams in the safe use of enterprise-authorized AI capabilities within the work environment for reliability engineering workflows, including validation habits and awareness of data sensitivity. Hands-on experience using enterprise-authorized AI-assisted software development tools (coding, test creation, troubleshooting, documentation) with the ability to validate and refine AI outputs for correctness, performance, and security. Understanding of responsible AI use in engineering workflows (data sensitivity, secure handling of inputs/outputs, resiliency and security expectations) and ability to guide peers on safe and effective usage. Overall knowledge of the Software Development Life Cycle, including design, build, test, release, and run disciplines. Solid understanding of agile methodologies and CI/CD, with focus on application resiliency and security practices. Proficiency in at least one programming language and ecosystem relevant to building services (e.g., Java/Spring Boot; familiarity with testing practices and frameworks). Ability to set and reinforce organization-level practices for reviewing AI-assisted recommendations, escalating uncertain decisions, and maintaining resiliency, security, and auditability outcomes. Ability to set and reinforce organization-level practices for reviewing AI-assisted recommendations and escalating uncertain decisions while maintaining resiliency, security, and auditability outcomes. Preferred Qualifications, Capabilities, and Skills Experience creating scalable microservices using Java, Spring Boot, and Kafka for financial services or similarly regulated environments. Experience using CI/CD toolsets including Git, Maven, Jenkins, SonarQube, and strong automated testing practices (JUnit, Mockito). Experience developing and maintaining deployment, monitoring, and operations automation, including incident response tooling and runbook automation. Strong working knowledge of observability patterns and tools (metrics, logs, traces) to troubleshoot complex production issues. Experience coaching teams to adopt AI coding assistants (e.g., GitHub Copilot, Cursor, Claude) as standard practice while enforcing secure coding and validation habits.