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Tier-2 brand, metro location, and mid-level generalist engineering title increase candidate competition.
LLM and RAG engineering skills are widely transferable across industries, making background fit relatively low sensitivity.
Strong mandatory LLM, RAG, Python, FastAPI, and vector DB skill requirements increase filtering strictness.
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Design, develop, and deploy enterprise Generative AI solutions including AI-powered applications, intelligent agents, and Retrieval-Augmented Generation (RAG) solutions.
Develop AI orchestration workflows using frameworks like LangChain, LangGraph, CrewAI, AutoGen, and Model Context Protocol and integrate AI services such as Azure OpenAI, OpenAI, Anthropic Claude into enterprise platforms.
Support deployment, monitoring, continuous improvement, and responsible AI practices including guardrails, evaluation frameworks, and hallucination mitigation.
Strong programming skills in Python with experience developing scalable REST APIs using FastAPI or similar frameworks.
Hands-on experience with Generative AI, Large Language Models (LLMs), Prompt Engineering, and building RAG, Agentic AI, and Multi-Agent applications.
Experience with AI orchestration frameworks (LangChain, LangGraph, CrewAI, AutoGen) and AI services integration (Azure OpenAI, OpenAI, Anthropic Claude).
Work Experience Required: Not explicitly mentioned in the JD.
Experienced in architecting and deploying complex Generative AI enterprise solutions with measurable impact on business processes.
Skilled at collaborating across technical and business teams to translate business requirements into AI-driven solutions.
Comfortable working in environments requiring hands-on development, technical evaluations, continuous improvement, and adherence to responsible AI principles.