Java Backend Developer (Java 17+, Microservices, Gen AI/LLM, RAG)

Top 3 Required Skills:

• Java
• Spring Boot
• Microservices
• Angular
• AI Role Description:
• 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 including:
• Prompt templates and prompt pipelines
• Retrieval-Augmented Generation (RAG) services
• 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 5+ years of hands-on experience in Java backend development Expertise in Java 11/17+, Spring Boot, Spring MVC, Spring Security Experience in Microservices, REST APIs, and API design (OpenAPI/Swagger) Experience with containers and cloud platforms (Docker, Kubernetes, Open. Shift, 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, chain-of-thought prompting
• Prompt templates and 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 BFSI environments