Hybrid (based in Reading UK)
Contract
Our client is seeking a versatile Backend / AI Developer with hands-on experience delivering end-to-end solutions from concept to production.
The ideal candidate brings strong expertise in Java (Spring Boot) and Python, with the ability to build scalable microservices, SaaS platforms, and AI-powered applications. You should be proficient in cloud platforms such as AWS and GCP, and experienced in API-first design, security best practices, and observability tools to ensure reliable, high-performing systems. We're seeking a versatile Backend / AI Developer with hands-on experience delivering end-to-end solutions from concept to production.
On the AI side, you have practical experience working with large language models (LLMs), prompt engineering, and frameworks like LangChain, with knowledge of advanced patterns such as Retrieval-Augmented Generation (RAG) and agent-based systems. A strong foundation in Agile SDLC practices is essential, along with familiarity in containerization, DevOps, and low-code/no-code platforms.Experience across both backend and AI domains will enable you to seamlessly integrate advanced AI capabilities into scalable enterprise systems, making you a key contributor to building the next generation of intelligent, secure, and high-performance applications.
Requirements:
- Core Development:Core Development:Hands-on experience in taking solutions from concept to production, with strong expertise in Java (Spring Boot) and Python for backend and AI-driven application development.
- Proficiency in building microservices, SaaS solutions, and API-first architectures to enable scalable, interoperable systems.
- Frameworks & Tools:Backend: Spring Boot (web, data, cloud, security), JUnit 5, Mockito, database integration.
- AI: LangChain, Retrieval-Augmented Generation (RAG), MCP servers (as consumer and developer), and prompt engineering for LLM optimization.
- Exposure to popular libraries and frameworks (Apache Commons, Guava, Swagger, TestContainers).
- Architecture & Platforms:Skilled in designing and deploying distributed systems on cloud hyperscalers (AWS, GCP).
- Familiarity with containerization (Docker), CI/CD pipelines, DevOps practices, and Infrastructure as Code (IaC).
- Experience with workflow/orchestration engines and agent-based architectures.
- Experience with workflow/orchestration engines and agent-based architectures.
- Scalability, Data & Observability:Expertise in designing for scalability, performance, and extensibility across structured, semi-structured, and unstructured data.
- Strong foundation in security best practices, with practical knowledge of logging, metrics, tracing, distributed debugging, and performance monitoring.
- Awareness of AI application security and model safety considerations.
- AI Development: End-to-end development of AI-powered solutions, from concept to production readiness.
- Practical experience with LLMs and agentic AI, including LangChain integration, RAG pipelines, prompt engineering, and model consumption via APIs.
- Ability to bridge backend and AI workflows, integrating advanced AI capabilities into scalable enterprise platforms.
- SDLC & Agile Practices: SDLC & Agile Practices: Familiar with the Software Development Life Cycle (SDLC) and effective collaboration in Agile environments.
- Ability to leverage low-code/no-code platforms for rapid solution delivery.
- Knowledge of Domain-Driven Design (DDD) and schema-first development.
- Experience designing plugin architectures and extensibility frameworks.
- Exposure to frontend development (HTML5, CSS3, TypeScript, responsive UIs) for full-stack collaboration.
