Build, iterate, andmaintain LLM-powered applications - including chatbots, document processingpipelines, predictive analytics interfaces, and intelligent search systems
Design and optimise RAG(Retrieval Augmented Generation) pipelines: chunking strategies, embeddingmodel selection, retrieval tuning, and context window management
Develop and refine promptengineering frameworks - maintaining prompt libraries, evaluating promptperformance, and implementing prompt versioning
Fine-tune open-weightmodels (Llama, Mistral) on YCH-specific logistics data to improve domainaccuracy and reduce inference costs
Integrate LLM capabilitiesinto YCH's legacy Java application APIs - building clean abstraction layersthat allow AI features without full system rewrites
Implement LLM evaluationpipelines using automated scoring (faithfulness, relevance, hallucination rate)and human evaluation frameworks
Collaborate with BusinessAnalysts to translate new use case specifications into production AI features
Contribute to internal AIdocumentation, prompt libraries, and reusable component libraries
Job Requirements:
1+ years working or projectexperience on LLM or AI application development
User InterfaceEngineering: Build dynamic, real-time UI components using React and Next.js tohandle AI streaming responses, multi-step agent status indicators, and markdownformatting.
AI BackendOrchestration: Design scalable backend API routes and background event queuesusing Node.js and TypeScript.
Hands-on experience withLLM frameworks: LangChain, LangGraph, LlamaIndex, or equivalent
Understanding of RAGarchitecture, vector databases, and embedding models
