Java Full Stack Developer

Project Responsibilities

Design, develop, and maintain high-performance Java-based backend services and APIs and front-end applications as well.
Deliver across the full SDLC leveraging AI coding tools (Claude Code, GitHub Copilot, etc.) as a core part of daily workflow
Participate in architectural discussions and technology selection
Perform code reviews and mentor junior engineers
Collaborate with frontend, data, and product teams to deliver cohesive solutions
Contribute to DevOps practices including CI/CD pipeline management and cloud deployments

Candidate's Portrait

Minimum 7 years of experience in backend software development
Preferably 3 years participating in software architecture and technology selection
Preferably experience working in cross-functional agile teams

Must-haves

Java 17 or later — strong proficiency required
Java Streams and functional programming patterns
Spring Framework: Core IoC, Spring MVC, Spring Boot, Spring Data & Repositories
JPA & Hibernate (ORM and query optimisation)
RESTful API design, JSON, JSON Schema, YAML
Microservices architecture and design patterns
Messaging platforms: Apache Kafka, RabbitMQ, or Apache ActiveMQ
Maven, Tomcat, JUnit & Mockito
AI-Augmented Development — Mandatory Core Expectation
Proficient daily use of AI coding assistants (Claude Code, GitHub Copilot, Cursor, or equivalent)
Leverage AI tools across the full SDLC: design, coding, review, testing, documentation, and debugging
Prompt engineering skills to extract high-quality, production-relevant output from LLM-based tools
Responsible AI tool use: output verification, hallucination awareness, and code quality assurance
Highly desirable - Frontend Exposure
Practical experience with Angular (v2+) or similar modern frontend frameworks
Working knowledge of TypeScript / JavaScript, HTML/CSS
Ability to read, review, and contribute to frontend codebases (not expected as a full-stack expert)
DevOps & Cloud
Cloud platforms — Azure preferred (AWS or GCP acceptable)
Git-based version control and branching strategies
CI/CD pipelines: GitHub Actions, Azure DevOps, or equivalent
Containerisation: Docker; Kubernetes exposure is a plus
Agile methodology and sprint-based delivery
General Engineering
Strong problem-solving and analytical thinking
Code review experience and ability to enforce engineering standards
Postman or equivalent API testing tools
Good spoken and written English
AI & Data Science Exposure
Familiarity with LLM integration patterns: RAG, prompt chaining, or agent frameworks (e.g. LangChain)
Exposure to Python for data manipulation or ML pipeline interaction
Experience integrating with AI/ML-powered services
Understanding of vector databases or semantic search concepts

Nice-to-have

Swagger / OpenAPI or RAML for API documentation
Static code analysis tools (e.g. SonarQube)
Cucumber for BDD testing
Infrastructure as Code: Bicep or Terraform (Azure preferred)