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, 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, 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
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