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In this role, you will design, develop, integrate, and support enterprise-grade Java microservices while contributing to an AI-enabled software development lifecycle. You will work closely with architects, Tech Leads, QA, SRE, and platform teams to deliver secure, scalable, and resilient solutions in a complex, highly regulated environment.\nYour tasks\nBuild, test, release, and support Java microservices and related components\nContribute to solution design and integration patterns aligned with target architecture and non-functional requirements\nIdentify and help resolve technical risks, dependencies, and delivery blockers\nFollow and contribute to engineering standards, CI/CD practices, and quality controls\nDeliver robust unit, integration, and contract testing\nParticipate in design reviews, code reviews, and security reviews\nUse AI-assisted engineering tools while maintaining security, quality, and governance standards\nApply GenAI development practices including LLM usage, RAG concepts, and agent-based workflows\nBuild production-ready services with strong observability and operational readiness\nSupport release activities, integrations, and environment readiness\nCollaborate with engineering, architecture, QA, platform, and operations teams\nContribute to continuous improvement initiatives and engineering excellence\nYour skills\nAt least 8 years of professional software engineering experience\nStrong hands-on experience with Python development in enterprise-scale environments\n\nGood practical knowledge of Java and experience working with Java microservices based on Spring Boot\n\nExperience working in enterprise-scale environments\nStrong knowledge of Oracle or PostgreSQL databases\nExperience with schema design, performance optimization, and transactional systems\nStrong unit and integration testing experience using tools such as JUnit, Mockito, and Testcontainers\nExperience with API and contract testing\nHands-on experience with Azure cloud services\nExperience with CI/CD pipelines and quality gates\nKnowledge of Jenkins or GitLab\nUnderstanding of Sonar, SAST, DAST, and dependency scanning\nStrong understanding of REST APIs, messaging, event-driven architectures, and integration patterns\nExperience contributing to technical design decisions and architectural discussions\nLeadership experience and strong collaboration skills\nPractical exposure to GenAI tools and software engineering use cases\nUnderstanding of LLM concepts, RAG patterns, AI governance, and engineering controls\nKnowledge of secure coding practices and security-by-design principles\nNice to have\nExperience in financial services or other highly regulated industries\nKnowledge of containers and Kubernetes\nExposure to service mesh technologies\nExperience with observability platforms, logging, metrics, and tracing\nExperience with performance engineering and resilience testing\nPrevious experience implementing AI-enabled SDLC practices\nHands-on experience building RAG solutions or integrating GenAI capabilities\nExperience with prompt engineering, evaluation frameworks, or AI governance controls\nWe offer\nHybrid work in our 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