Developer – AI-Enabled Delivery

Mindbox is a tech-driven company connecting top IT talents with technology projects for leading enterprises across Europe, focusing on building meaningful digital solutions with consulting, engineering, and talent.

• Strong experience building Java microservices with Spring Boot in enterprise environments
• Proficiency in RDBMS such as Oracle and PostgreSQL, including schema design and performance tuning
• Solid experience with unit, integration, and contract testing
• Experience with CI/CD pipelines and quality gates using tools like Jenkins, GitLab, Sonar, SAST, and DAST
• Understanding of integration patterns including REST and messaging/event-driven, API design and versioning
• Hands-on experience with cloud platforms, preferably Google Cloud Platform
• Knowledge of GenAI concepts and software engineering tools such as LLMs, RAG patterns, and governance controls

Nice to have:

• Delivery experience for regulated financial services platforms with audit and operational requirements
• Familiarity with cloud-native patterns including containers, Kubernetes, service mesh, and observability tooling
• Experience with performance engineering and resilience testing
• Prior involvement in implementing AI-enabled SDLC practices achieving measurable productivity gains
• Hands-on experience with GenAI prototyping or integration, including RAG pipelines and prompt lifecycle management

• Build, test, deploy, and maintain Java microservices and related components across multiple product areas
• Contribute to solution design and integration patterns aligned with target architecture and non-functional requirements
• Identify and resolve technical risks, dependencies, and delivery blockers collaboratively
• Apply and contribute to engineering standards including coding best practices, branching strategy, CI/CD quality gates, test strategy, and definition of done
• Deliver automated tests (unit, integration, contract) using tools like JUnit, Mockito, and Testcontainers
• Participate in code reviews, design reviews, and security reviews ensuring remediation of findings
• Use AI-assisted tools (e.g., GitHub Copilot) responsibly, ensuring code quality, security, and IP protection
• Apply governance for AI-generated code: traceability, audit readiness, secure coding reviews, LLM safe practices, RAG techniques, and agent-based workflows with human-in-the-loop controls
• Embed security-by-design principles: secure coding, vulnerability management, and compliance
• Deliver production-ready solutions supporting observability (logs, traces, metrics) and readiness for incident handling
• Support release activities and integration during environment cutovers
• Collaborate with Tech Leads, QA, SREs, and architecture teams to deliver integrated, high-quality solutions
• Promote a culture of continuous improvement and cross-functional collaboration