JPMorgan Chase—Software Engineer II-Java, Kubernetes

Job description

Description: You’re ready to gain the skills and experience needed to grow within your role and advance your career — and we have the perfect software engineering opportunity for you. Job responsibilities - Executes standard software solution, design, development, and technical troubleshooting - Writes secure and high-quality code using the syntax of at least one programming language with limited guidance - 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. - 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, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation - Applies technical troubleshooting to breakdown solutions and solve technical problems of basic complexity - Gathers, analyzes, and draws conclusions from large, diverse data sets to identify problems and contribute to decision-making in service of secure, stable application development - Learns and applies system processes, methodologies, and skills for the development of secure, stable code and systems Required qualifications, capabilities, and skills - Formal training or certification on software engineering concepts and 4+ years applied experience - Foundational SRE/ops knowledge: Understanding of Linux fundamentals, networking basics (DNS, HTTP/S, TCP/IP), and how distributed systems fail (timeouts, retries, dependency issues). - Programming/scripting ability: Ability to write and maintain automation in at least one language (e.g., Python, Java, Go, or Bash) plus comfort with Git and basic CI/CD concepts. - Monitoring & troubleshooting skills: Hands-on experience with logs/metrics/traces, building dashboards/alerts, and using a structured approach (hypothesis-driven debugging, runbooks) to triage production issues. - Cloud/container familiarity: Exposure to cloud platforms and modern runtime environments (containers, Kubernetes) and understanding of deployments, configuration, and secrets management . - Baseline knowledge of software, applications and technical processes within a given technical discipline (e.g., cloud, artificial intelligence, Android, etc.) - Proficiency in developmental toolsets - Basic knowledge of industry-wide technology trends and best practices - Working knowledge of using enterprise-authorized AI capabilities within the work environment to support software engineering workflows with strong validation habits and awareness of data sensitivity - Ability to review and validate AI-assisted code and technical recommendations before use, escalating when uncertain and following security and data handling requirements Preferred qualifications, capabilities, and skills - Exposure to cloud technologies - Experience with cloud platforms (e.g., AWS/Azure/Google Cloud) is a plus