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)