Full Stack Engineer

Key Responsibilities

• Design, develop, and maintain enterprise-grade full-stack applications using Java (Spring Boot) and React.js.
• Design, build, and maintain scalable microservices and event-driven architectures.
• Develop and integrate RESTful APIs and ensure seamless communication between frontend, backend, and external services.
• Design and optimize database architecture, distributed systems, and batch processing/scheduling solutions.
• Troubleshoot and resolve complex issues across multiple applications and microservices.
• Make technical and architectural decisions that ensure scalability, maintainability, and long-term system performance.
• Collaborate with cross-functional teams, translating technical concepts for both technical and non-technical stakeholders.
• Participate in code reviews and promote software engineering best practices, security, and code quality.
• Contribute to cloud-native deployments using Azure, Docker, and Kubernetes.
• Support DevSecOps practices, including CI/CD pipelines, monitoring, and application observability.
• Mentor junior engineers and provide technical leadership on complex initiatives.
• Integrate AI capabilities into enterprise applications using LLM APIs (e.g., OpenAI, Azure OpenAI), RAG pipelines, and AI orchestration frameworks such as LangChain and LangGraph.
• Ensure AI solutions follow responsible AI principles, including security, governance, data privacy, and output validation.

Required Qualifications

• Bachelor's degree in Computer Science, Software Engineering or equivalent experience
• 10+ years of experience in building robust enterprise grade applications
• Strong proficiency in Java and Spring Framework (Spring Boot, Spring MVC, Spring Cloud, Spring Security)
• Extensive experience with React.js including hooks, state management (Redux) and React Component lifecycle
• Strong foundation in modern web technologies include JavasScript (ES6+), HTML 5 and CSS3
• Proven track record designing, implementing, and maintaining microservices architectures at scale
• Strong background in event-driven architecture and messaging systems (Solace, Kafka, RabbitMQ, or similar)
• Demonstrated ability to work independently with minimal oversight while taking full ownership of complex projects
• Strong architectural mindset with proven ability to make strategic technical decisions that balance immediate requirements with long term scalability and maintainability
• Experience with RESTful API design, database architecture, and distributed system patterns
• Strong problem-solving skills and ability to debug complex issues across multiple services
• Solid experience in developing and maintaining enterprise scheduling solutions and batch processing applications
• Strong communication skills with the ability to articulate technical terms to both technical and non-technical stakeholders

Preferred Qualifications

• Experience with cloud platforms (Azure) and containerisation (Docker, Kubernetes)
• Experience with DevSecOps and CI/CD pipelines
• Experience with mentoring and leading technical initiatives
• Experience with monitoring and observability tools (Azure Monitoring, DynaTrace)
• Deep understanding of cybersecurity best practices and performance optimisation techniques
• Familiarity with Agile methodologies and experience working in fast-paced environments
• Practical experience integrating AI capabilities into enterprise applications, including working with LLM APIs (e.g. OpenAI, Azure OpenAI, or equivalent)
• Proficiency in AI-assisted development practices, including the effective use of AI coding tools (e.g. GitHub Copilot, Claude) to accelerate delivery without compromising code quality or security
• Experience designing and deploying RAG (Retrieval-Augmented Generation) pipelines or agentic AI workflows within enterprise environments
• Familiarity with AI orchestration frameworks such as LangChain, LangGraph and Microsoft Agent Framework
• Understanding of responsible AI principles, including prompt injection risks, output validation, data privacy considerations, and AI governance frameworks
• Experience with vector databases (e.g. Pinecone, Chroma, pgvector) and embedding models for semantic search and knowledge retrieval use cases