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Niche LLM skills but metro locations and attractive AI title produce moderate applicant competition.
Core LLM and MLOps skills transfer across industries, but specialized tooling and fine-tuning experience raise domain sensitivity.
Extensive mandatory LLM, LangChain, vector DB, cloud, agent development and MLOps requirements increase screening strictness.
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Design, develop, and deploy scalable AI/ML and Generative AI applications using Python and Large Language Models (LLMs).
Develop AI agents and autonomous workflows using frameworks like LangChain, LangGraph, CrewAI, or AutoGen and integrate these AI models with enterprise applications via APIs and microservices.
Implement Retrieval-Augmented Generation (RAG) solutions with vector databases; monitor and optimize AI model performance in production using MLOps practices.
Strong hands-on experience in Python development focused on AI/ML and Generative AI.
Experience with Large Language Models (e.g., GPT, Claude, Llama), AI agent frameworks (CrewAI, AutoGen), and vector databases (Pinecone, ChromaDB, Weaviate, FAISS).
Proficiency in prompt engineering and RAG architecture implementation.
Work Experience Required: Not explicitly mentioned in the JD.
Experienced in deploying AI solutions in cloud environments with knowledge of Azure OpenAI, AWS Bedrock, or Google Vertex AI.
Skilled in applying MLOps practices including CI/CD, Docker, Kubernetes, and DevOps tools to maintain AI model lifecycle.
Capable of translating complex business requirements into technical AI solutions and collaborating with cross-functional teams.