Software Engineer II-Java, Kubernetes

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