Full Stack Engineer - AI Developer
Posted Updated
Summary
Senior full-stack engineer who builds and modernises enterprise applications (Java/Spring Boot, Drupal/PHP, TypeScript with Angular/React/Vue) and weaves AI coding tools like Claude Code and MCP integrations into secure development workflows, from CI/CD to code review.
We are looking for a Senior Full-stack Engineer to develop and maintain enterprise applications, modernise legacy systems, and integrate AI tools into everyday development workflows.
Key Responsibilities
- Design, develop, and maintain front-end and back-end applications, APIs, and databases.
- Use AI coding assistants for development, debugging, refactoring, testing, and documentation.
- Prepare and modernise existing codebases for AI-assisted development, ensuring clear documentation, coding standards, and reliable tests.
- Set up secure AI development environments and define project instructions to guide AI-generated code.
- Configure and extend Model Context Protocol (MCP) integrations to connect AI agents with approved tools and enterprise systems.
- Review AI-generated code for quality, security, performance, and compliance with project requirements.
- Integrate automated testing and security checks into CI/CD pipelines.
- Troubleshoot AI development workflows and help teams adopt effective and secure AI practices.
Required Skills and Experience
- Senior experience in full-stack software development, from architecture and coding to testing and deployment.
- Strong knowledge of Java / Spring Boot, Drupal / PHP, and TypeScript, with Angular, React, or Vue.
- Hands-on experience with AI development tools, preferable Kiro and Claude / Claude Code.
- Experience modernising legacy applications and preparing repositories for AI-assisted development.
- Good understanding of LLMs, prompt engineering, context management, and AI coding agents.
- Hands-on experience with MCP clients and servers, including configuration, extension, and secure access.
- Knowledge of semantic code search, embeddings, and Retrieval-Augmented Generation (RAG).
- Experience with enterprise AI platforms or model APIs and their secure configuration.
- Strong knowledge of Git, REST APIs, databases, authentication, and secure integrations.
- Experience with Docker, Kubernetes, or Dev Containers.
- Strong knowledge of CI/CD, DevSecOps, automated testing, and code security checks.
- Awareness of AI-related security risks, software supply-chain security, data protection.
- Certifications in cloud development, Generative AI, AI engineering, DevSecOps, cloud security are considered an advantage.