Fullstack Java Software Engineer III

JPMorgan Chase & Co.•Plano, TX

12h

About The Position

As a Software Engineer at JPMorganChase within the Commercial & Investment Bank, you are part of an agile team that works to enhance, design, and deliver the software components of the firm’s state-of-the-art technology products in a secure, stable, scalable, and observable way. As an emerging member of a software engineering team, you execute software solutions through the design, development, and technical troubleshooting of multiple components within a technical product, application, or system—while also partnering closely with engineering, security, and operations to enable fast, reliable software delivery through modern DevOps practices.

Requirements

3+ years’ proficient experience.
Hands-on practical experience in system design, application development, testing, and operational stability.
Experience developing, debugging, and maintaining code in a large corporate environment with one or more modern programming languages and database querying languages.
Demonstrable ability to code in one or more languages.
Experience across the whole Software Development Life Cycle.
Exposure to agile methodologies such as CI/CD, Application Resiliency, and Security.
Foundational DevOps capabilities, including working knowledge of CI/CD concepts, environment automation, and release/deployment practices.
Basic observability understanding (monitoring/logging/alerting) and an interest in improving reliability and operational outcomes.
Emerging knowledge of software applications and technical processes within a technical discipline (e.g., cloud, artificial intelligence, machine learning, mobile, etc.)
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.

Nice To Haves

Familiarity with modern front-end technologies.
Exposure to cloud technologies.
Experience with Infrastructure as Code and configuration automation tooling.
Experience improving CI/CD pipelines (build/test automation, deployment automation, quality gates).
Familiarity with operational excellence practices (dashboards, SLO/SLI concepts, incident management, post-incident remediation).

Responsibilities

Executes standard software solutions, including design, development, and technical troubleshooting.
Writes secure and high-quality code using the syntax of at least one programming language with limited guidance.
Designs, develops, codes, and troubleshoots with consideration of upstream and downstream systems and technical implications.
Applies knowledge of tools within the Software Development Life Cycle toolchain to improve the value realized by automation.
Builds, maintains, and improves CI/CD pipelines to standardize build, test, deployment, and release processes.
Automates infrastructure and environment setup using Infrastructure as Code (IaC) to enable repeatable, reliable provisioning and configuration.
Standardizes deployment and release practices, including safe rollouts (e.g., phased/canary) and rollbacks, to reduce delivery risk.
Implements monitoring, logging, alerting, and dashboards to improve observability and operational insight.
Supports incident response, participates in post-incident reviews, and contributes to remediation and reliability improvements.
Collaborates closely with developers, security, and operations to embed security and compliance controls into the delivery process (DevSecOps).
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.