Developer – AI-Enabled Delivery

Mindbox connects top IT talents with technology projects for leading enterprises across Europe. This position is part of a team building Global Collateral and Facility Management capabilities for a mission-critical platform replacement program, aiming to deliver a modern in-house solution leveraging Java microservices, cloud technologies, and AI-assisted engineering practices with strong governance and quality controls.

• 4 to 8 years of experience.
• Strong hands-on experience with Java microservices development using Spring Boot.
• Solid understanding of RDBMS (Oracle, PostgreSQL) including schema design, performance tuning, and transaction handling.
• In-depth knowledge of unit testing, integration testing, contract testing with JUnit, Mockito, Testcontainers.
• Proven experience with CI/CD pipelines (Jenkins, GitLab) and quality gates including Sonar, SAST, and DAST scanning.
• Familiarity with integration patterns such as REST APIs and event-driven messaging.
• Experience in technical design with pragmatic trade-offs under delivery constraints.
• Hands-on experience with cloud platforms (GCP).
• Practical exposure to GenAI concepts and tools including LLMs, RAG, agentic workflows, and associated governance.

Nice to have:

• Experience in regulated industries with strong audit and operational control.
• Knowledge of cloud-native patterns: containers, Kubernetes, service mesh, observability tooling.
• Exposure to performance engineering and resilience testing.
• Prior participation in AI-enabled SDLC adoption with governance frameworks.
• Hands-on with GenAI prototyping including prompt engineering, RAG pipelines, and evaluation methods.

• Design, build, test, and release Java microservices and related components in an enterprise environment.
• Contribute to solution design and integration patterns, ensuring alignment with architecture and non-functional requirements.
• Identify and mitigate technical risks and dependencies impacting delivery.
• Apply and enhance engineering standards including coding practices, branching, TDD, CI/CD pipelines, and automated quality gates.
• Deliver comprehensive unit, integration, and contract tests using JUnit, Mockito, and API testing.
• Conduct peer reviews for design, code, and security issues and remediate them promptly.
• Use AI-assisted tooling (e.g., GitHub Copilot) responsibly to accelerate delivery while ensuring security and traceability.
• Apply guardrails for AI-generated code including review, audit, and IP compliance.
• Contribute to safe GenAI integration patterns such as LLM usage, retrieval-augmented generation (RAG), and agent workflows with human review.
• Embed security-by-design including vulnerability management and secure coding practices with SAST and DAST.
• Ensure production readiness including observability, logging, monitoring, performance testing, and incident support.
• Collaborate with cross-functional teams to deliver integrated outcomes and improve developer velocity.
• Promote an inclusive, collaborative culture based on engineering excellence and innovation.