Responsibilities will include: 

• Running the initial pilot and technical discovery.
• Scaling the capability across approximately 250 microservices.
• Building reusable automated regression and integration testing capabilities.
• Onboarding APIs to Renovate for automated dependency patching.
• Completing the remaining Spring Boot 4 migration, including approximately 75 APIs requiring migration from Spring Boot 3.
• Establishing reusable AI-enabled engineering workflows and accelerators.
• Ultimately transitioning the capability into Level 3 operational support
• Establish engineering standards across development, testing, security and CI/CD

Skills & Experience:

• Combination of deep recent hands-on Java/Spring Boot engineering expertise with genuine Engineering Lead experience and practical AI-assisted engineering capability
• Demonstrated technical leadership experience of engineering teams
• Strong integration engineering experience, including APIs, microservices and event-driven architectures.
• Experience with event streaming/Kafka and high-throughput, low-latency integration environments.
• Deep understanding of unit, integration, contract, regression and end-to-end testing.
• Experience creating reusable automated test frameworks, test harnesses or engineering accelerators.
• Strong CI/CD, Git and automated quality-gate experience.
• Experience with dependency-management automation such as Renovate or comparable tooling.
• Experience leading Java/Spring Boot platform upgrades across multiple services.
• Kubernetes and cloud-based microservice experience.
• Strong understanding of application security, dependency vulnerabilities, observability, production support and operational resilience
• Ability to define engineering standards, reference architectures and implementation patterns and then lead engineers through their adoption.
• Experience working with on shore and off shore teams and third party vendors
• Experience applying AI practically across the software engineering lifecycle
• Using AI to improve vulnerability remediation or dependency upgrades.
• Establishing repeatable AI-assisted engineering practices for a broader development team.
• Codex or comparable AI engineering tools
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