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Hot LLM role with mid-level demand and common title increases applicant density despite niche skills.
Specialized LLM, RAG, and vector database skills limit transferability across non-AI-focused roles.
Explicit 2–3 year requirement plus mandatory LLM, LangChain, FastAPI, and vector DB experience filter strictly.
Design, develop, and maintain AI applications leveraging Large Language Models (LLMs) and frameworks like LangChain or LangGraph.
Build and optimise Retrieval-Augmented Generation (RAG) pipelines, semantic search, and scalable backend services using Python and FastAPI.
Integrate AI models, REST APIs, vector databases, and third-party services into production-ready AI-driven features, collaborating with cross-functional teams.
2–3 years of professional Python software development experience.
Hands-on experience with Large Language Models and LLM orchestration frameworks (LangChain, LangGraph, or similar).
Experience developing REST APIs with FastAPI or comparable Python frameworks.
Familiarity with RAG concepts, embeddings, vector search, vector databases (e.g., Chroma, FAISS, Pinecone), and cloud platforms (AWS, Azure, or GCP).
Strong Python programming foundation with practical LLM and AI application development experience.
Experience working with vector databases, AI model integration, and building production-ready backend services.
Demonstrated ability to work across teams to prototype, test, and deploy AI-powered solutions with good software engineering discipline.