Identifies opportunities to eliminate or automate remediation of recurring issues to improve overall operational stability of software applications and systems
Leads evaluation sessions with external vendors, startups, and internal teams to drive outcomes-oriented probing of architectural designs, technical credentials, and applicability for use within existing systems and information architecture
Required qualifications, capabilities, and skills
Formal training or certification on software engineering concepts and 5+ years applied experience
Strong hands-on Java + Spring Boot engineering experience building secure, production-grade services and RESTful APIs.
Experience with event-driven/messaging systems (e.g., Kafka, JMS, RabbitMQ) and building reliable integrations.
Full-stack capability with ReactJS + modern JavaScript/HTML/CSS, delivering maintainable UI components using a governed UI framework/design system.
Technical leadership with cloud-native delivery and engineering excellence: leading solution design/architecture; driving design/code reviews and standards; owning SDLC quality via automated testing (JUnit + integration/contract testing), CI/CD (Jenkins or equivalent), and production troubleshooting; and delivering/operating on cloud platforms (AWS or Cloud Foundry) with observability (logs/metrics/traces) and production readiness.
Experience with Spring Framework and Hibernate/JPA, Experience building UI using ReactJS, JavaScript, HTML, and CSS.
Strong testing experience (JUnit and broader automated testing practices such as integration/contract/component testing).
Strong SQL skills and experience with RDBMS such as Oracle, MySQL, MS SQL, or Amazon RDS, Experience with source control (Git/Bitbucket/GitHub) and modern review/branching workflows.
Demonstrated experience leading effective use of approved AI-assisted software development tools (e.g., for coding, code review, test acceleration, troubleshooting) with the ability to set team expectations for validating AI outputs for correctness, performance, and security.
Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; experience coaching engineers on safe, compliant adoption within delivery practices
Preferred qualifications, capabilities, and skills
Experience with microservices architecture patterns (service boundaries, resiliency, backward compatibility, idempotency).
Experience with micro-frontend architectures (frontend decomposition, shared components, independent deployment/versioning).
Experience with blue-green deployments (deployment strategies to reduce risk/downtime, rollback planning, release validation).
