Java Software Engineer

o Involved in a team who manages core system development. o Participate in the overall application lifecycle: requirements analysis, design, development, testing, deployment, and maintenance. o Write and perform unit and integration tests; leverage AI-driven test generation tools. o Perform code reviews prior to every pull request and/or merge request. o Define and communicate technical and design requirements; estimate effort in Story Points. o Troubleshoot and debug applications; participate in on-call/standby rotations for production support. o Build and maintain AI-powered workflows using LangChain, Semantic Kernel, or similar frameworks o Develop and deploy Retrieval-Augmented Generation (RAG) pipelines for document Q&A and contextual chat o Integrate LLM APIs (e.g., OpenAI, Azure OpenAI) into backend services and user interfaces Requirement Fluent in English, both written and verbal; experienced in at least 3 full- cycle, enterprise-scale or cross-country projects using English as the working language. 5+ years professional experience in Java development, with Spring Boot as the primary framework. In-depth knowledge of microservice architecture and REST API development using Spring Boot. Strong understanding of resilience patterns (retries, circuit breakers, fallback methods) and scalable system design. Proficient with: ▪ Spring Framework and its modules ▪ ORM/SQL mappers (Hibernate, MyBatis) ▪ Relational and NoSQL databases ▪ Vector databases (Pinecone, pgsql, Azure AI Search) Experience with messaging systems (JMS, Kafka, RabbitMQ, Redis Streams). Deep expertise in Spring Security, OAuth2, SSO; ability to conduct security audits and remediate OWASP Top Ten vulnerabilities. Mastery of Spring Batch: job orchestration, scheduling, monitoring, error handling, transaction management, and external system integration. Skilled in analyzing and modernizing legacy codebases. Expertise in JVM performance tuning and concurrency troubleshooting. Solid grasp of design patterns (GoF, EIP, Java EE, Microservices) and SOLID principles. Strong Linux foundations and scripting skills (Bash, Python); exposure to Docker and Kubernetes. Familiarity with Spring Cloud for distributed systems is a plus. Knowledge of monitoring and logging tools (Prometheus, Grafana, ELK, Splunk) and distributed tracing (Jaeger, Zipkin). Proficient with development toolchains: Git/SVN, CI/CD (Jenkins, TeamCity, GitLab CI), configuration management (Ansible, Chef, Puppet). Practical experience working with AI-MCP or similar AI-agent ecosystems; able to review, validate, and integrate AI-generated code. Experience integrating AI services or frameworks (OpenAI API, LangChain, AWS SageMaker) into production workflows and applying MLOps practices. Relevant certifications (Oracle Java, AWS/Azure/GCP, CISSP, CSSLP) are a plus.