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)