Java Backend Developer – Java (17+), Spring Boot, Microservices

Java BE Developer Java Spring Boot Microservices

Key Responsibilities

• Design develop and maintain high-performance backend services using Java (17) Spring Boot and Microservices architecture.
  
• Build and expose RESTful and event-driven APIs supporting enterprise-scale applications.
  
• Integrate Generative AI / LLM capabilities (e.g. text generation summarization Q&A classification) into backend workflows.
  
• Design test and optimize prompts and prompt orchestration strategies to ensure accuracy determinism and performance.
  
• Develop AI-aware backend components such as prompt templates and prompt pipelines Retrieval Augmented Generation (RAG) services and AI inference orchestration layers.
  
• Implement secure API integrations with AI platforms and internal data sources ensuring compliance with enterprise security standards.
  
• Apply prompt versioning evaluation and monitoring techniques to improve AI output quality over time.
  
• Ensure non-functional requirements including scalability resiliency performance and observability.
  
• Contribute to CI/CD pipelines containerization and cloud-native deployments.
  
• Participate in code reviews architecture discussions and technical design decisions.
  
• Support production systems and troubleshoot complex backend or AI integration issues.
  

Required Technical Skills

Core Backend Engineering

• Strong hands-on experience in Java backend development (5 years).
  
• Expertise in Java 11/17 Spring Boot Spring MVC Spring Security.
  
• Solid experience in Microservices REST APIs and API design (OpenAPI/Swagger).
  
• Experience with containers and cloud platforms (Docker Kubernetes OpenShift Azure/AWS).
  
• Strong knowledge of SQL and NoSQL databases (DB2 PostgreSQL MongoDB).
  
• Experience in CI/CD DevOps practices and automated testing.
  

AI & Prompt Engineering

• Hands-on experience integrating Large Language Models (LLMs) into backend systems.
  
• Strong understanding of prompt engineering techniques including zero-shot few-shot and chain-of-thought prompting.
  
• Experience with prompt templates dynamic prompt generation guardrails validation and hallucination reduction.
  
• Experience building RAG-based solutions using vector stores and embeddings.
  
• Familiarity with AI orchestration frameworks or SDKs (enterprise or open source).
  
• Ability to evaluate prompt and model responses for quality bias and consistency.
  

Security & Compliance

• Experience implementing OAuth 2.0 JWT SSL/TLS and secure API patterns.
  
• Awareness of data privacy PII handling and AI governance in regulated environments (BFSI preferred).