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Specialized GenAI skills but smaller employer and non-top metro create medium competition density.
GenAI engineering skills transfer across industries but require specialized LLM and vector database expertise.
Explicit 10+ years plus mandatory LLM, vector DB, and Python experience raises shortlisting strictness to high.
Build, optimize, and maintain end-to-end Retrieval-Augmented Generation (RAG) pipelines including document ingestion, chunking, embedding, vector indexing, retrieval, and reranking.
Integrate and deploy Large Language Model (LLM) APIs (e.g., Anthropic Claude, OpenAI, Gemini, open-source models) into scalable Python FastAPI backend services.
Design and implement agentic workflows, structured outputs, function/tool calling using frameworks like LangChain or LlamaIndex, with a focus on testing, logging, hallucination mitigation, and cost monitoring.
Strong proficiency in Python programming including async, REST APIs, and object-oriented design, and solid Git knowledge.
Hands-on experience with vector databases (Chroma, Qdrant, Pinecone, pgvector) and embedding models related to GenAI and RAG.
Daily active use of AI-assisted coding tools such as Claude Code CLI, Cursor, or GitHub Copilot with ability to guide AI agents and inspect generated code.
Work Experience Required: At least 1 shipped or working project beyond basic chat related to semantic search, custom RAG implementations, automated agents, or equivalent GitHub repositories.
Experienced with AI-native development workflows leveraging agentic coding tools for rapid development and deployment.
Comfortable working with LLM integration and scalable backend service construction using Python frameworks like FastAPI.
Familiar with end-to-end GenAI system components including vector DBs, embeddings, agent orchestration, and evaluation techniques to monitor model behavior and API costs.