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)
