Microservices Development: Maintain and evolve distributed architectures using Java 17/21 and Spring Boot 3.x, focusing on high availability and low latency.
AI and LLM Implementation: Integrate generative AI models (such as Gemini) into Java applications, using frameworks like LangChain4j or Spring AI to build automation flows and intelligent agents.
Cloud-Native Architecture on GCP: Develop and manage services using Cloud Run, GKE (Google Kubernetes Engine) and Cloud Functions, ensuring the application fully leverages managed infrastructure.
Data and Event Processing: Build resilient data pipelines using GCP Pub/Sub or Apache Kafka for asynchronous communication between services.
Database Optimization: Model and query relational databases (Cloud SQL/PostgreSQL) and NoSQL stores (Firestore or Bigtable), and work with vector databases for semantic search in AI projects.
Technical Leadership: Conduct rigorous code reviews, define design patterns and ensure test coverage (unit, integration and load tests).

Advanced experience with Java 11+ (preferably Java 17 or 21).
Proficiency with the Spring Framework (Boot, Security, Data, Cloud).
Experience with persistence tools: Hibernate/JPA and QueryDSL.
Solid knowledge of JUnit 5, Mockito and Testcontainers.
Hands-on experience with Vertex AI (Google’s API for AI models).
Experience implementing RAG (Retrieval-Augmented Generation) integrating Java with vector databases.
Familiarity with prompt engineering for optimizing language model responses within code.
Experience with Google Cloud Storage, Identity Platform (IAM) and Secret Manager.
Knowledge of observability: Cloud Logging, Cloud Monitoring and Trace.
Proficiency with CI/CD tools (GitHub Actions, GitLab CI or Google Cloud Build) integrated with GCP.

Senior Software Engineering Analyst – Java

Key skills