Senior Software Engineer - Java Cloud and AI

Job Responsibilities: • Executes software solutions including design, development, and technical troubleshooting, with the ability to think beyond routine or conventional approaches to build durable solutions and break down complex technical problems using Java, Spring, and Spring Boot • Creates secure and high-quality production code for microservices and REST APIs, and maintains synchronous and asynchronous processing components that integrate reliably with dependent systems • Produces architecture and design artifacts for complex applications (e.g., service boundaries, data flows, interface contracts, resiliency patterns) and is accountable for ensuring design constraints are implemented and validated through software development • Gathers, analyses, synthesizes, and develops visualizations and reporting from large, diverse datasets to drive continuous improvement in application performance, resiliency, and operational stability • Proactively identifies hidden issues and patterns in code, logs, and data, using these insights to improve coding hygiene, reduce defects, and strengthen system architecture and scalability • Contributes to software engineering communities of practice and technical forums that explore new and emerging technologies, patterns, and engineering standards • Adds to a team culture of diversity, opportunity, inclusion, and respect through collaboration, mentorship, and constructive feedback • 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 across build, test, release, and operations. Required Qualifications, Capabilities, and Skills: • Hands-on practical experience in system design, application development, testing, and operational stability for production systems • Demonstrable ability to code in Java with Spring and Spring Boot, including microservices architecture and REST API development • Experience developing, debugging, and maintaining enterprise-scale applications in a large corporate environment using one or more modern programming languages and database querying languages (MS SQL Server, Oracle, SQL) • Overall knowledge of the Software Development Life Cycle, including requirements, design, development, testing, release, and support • Solid understanding of agile delivery practices, including CI/CD, application resiliency, and security fundamentals • Demonstrated knowledge of software applications and technical processes within a technical discipline such as cloud (e.g., deploying and operating services in cloud or hybrid environments) • 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 • 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: • Experience building data products on Databricks using lakehouse patterns, including Delta Lake and medallion architecture • Familiarity with enterprise data platforms and data mesh principles, including domain-aligned datasets and well-defined data contracts • Exposure to data quality, observability, and metadata management practices and tools (data validation frameworks, lineage, monitoring, and alerting) • Experience enabling analytics, reporting, and AI/ML workloads through curated datasets, performance-optimized pipelines, and reliable service interfaces • Experience developing or supporting regulatory controls use cases, including auditability, traceability, and evidence-driven control outcomes • Experience with Infrastructure as Code using Terraform and/or CloudFormation, including environment provisioning and repeatable deployments • Experience designing event-driven architectures using AWS services such as SQS, SNS, and Event Bridge, including asynchronous processing and resiliency patterns.