Java developer - AI/LLM

KEY RESPONSIBILITIES

• Design, develop, and enhance backend services and APIs using Java and Spring Boot.

• Build high-throughput and resilient event-driven components using Kafka.

• Integrate Large Language Models (LLMs) such as OpenAI, Claude, or Gemini into backend services via REST APIs and SDKs.

• Build and maintain AI-powered microservices including RAG (Retrieval-Augmented Generation) pipelines, semantic search, and document intelligence features.

• Develop and expose AI agent workflows using frameworks such as LangChain4j, Spring AI, or similar Java- native AI toolkits.

• Implement prompt engineering strategies, context management, and output validation layers for LLM interactions.

• Design vector database integrations (Pinecone, Weaviate, pgvector) for embedding storage

• Participate in code reviews, refactoring efforts, and optimisation of system performance.

• Ensure best practices for coding standards, security, maintainability, and AI model governance.

• Troubleshoot production issues and provide root cause analysis and long -term fixes.

• Support CI/CD pipeline integration, model deployment automation, and MLOps tooling.

• Contribute to documentation, technical specifications, and architectural diagrams.

REQUIRED SKILLS & QUALIFICATIONS

• 5+ years of hands-on development experience in Java-based applications.

• Strong expertise in Java (8/11/17), Spring Framework, Spring Boot, and RESTful services.

• Experience with Kafka for messaging, streaming, or event-driven architecture.

• Practical knowledge of MongoDB or other NoSQL databases (e.g., Cassandra, DynamoDB, Couchbase).

• Solid understanding of microservices architecture and distributed systems.

• Hands-on experience consuming LLM APIs (OpenAI GPT-4o, Anthropic Claude, Google Gemini) in production Java applications.

• Familiarity with Spring AI or LangChain4j for building LLM-backed services in Java ecosystems.

• Experience with prompt engineering — crafting, versioning, and testing prompts for accuracy, safety, and

cost efficiency.

PREFERRED SKILLS

• Experience working in the Insurance domain (Policy, Claims, Underwriting, Billing, etc.).

• Hands-on experience building or fine-tuning ML models using Python-based frameworks (Hugging Face, scikit-learn, PyTorch) integrated with Java services.

• Exposure to AI agent orchestration tools — LangGraph, AutoGen, CrewAI, or OpenAI Assistants API.