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High due to mid-level demand, metro location, and hot GenAI skillset driving candidate interest.
Core ML/GenAI skills transfer across industries though vector/LLM tool expertise increases domain specificity.
Explicit 3–6 years plus mandatory LLM, LangChain, RAG, and vector DB experience.
Own management and debugging of production GenAI model pipelines with root cause analysis.
Design and build LLM-based workflows and Retrieval-Augmented Generation architectures using LangChain and vector databases like Pinecone.
Handle document ingestion and unstructured data extraction workflows using tools like AWS Extract and GenAI techniques.
3–6 years of experience in data science, applied ML, or GenAI roles with a strong project portfolio.
Bachelor’s or Master’s degree in Data Science, Computer Science, AI/ML, Statistics, Mathematics, or related fields.
Proficiency with machine learning frameworks such as scikit-learn, TensorFlow, or PyTorch.
Hands-on experience with LLMs, Generative AI frameworks, LangChain, prompt engineering, and practical knowledge of RAG patterns with vector embeddings and vector DBs like Pinecone.
Experienced in developing scalable, production-grade Generative AI systems and managing complex AI workflows.
Demonstrates strong analytical and technical skills in both AI model pipelines and unstructured data processing.
Comfortable collaborating with cross-functional teams and communicating technical insights to diverse stakeholders.