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Mid-level LLM data scientist role in a metro with broad skill requirements increases applicant competition.
Requires specialized Generative AI, LLM, and vector DB expertise, limiting cross-industry transferability.
Explicit 4–7 years plus many mandatory LLM, NLP, and deployment skills creates highly strict filters.
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Design, develop, and deploy end-to-end AI/ML solutions focusing on Generative AI and Large Language Models (LLMs).
Build and fine-tune LLM-powered applications and Retrieval-Augmented Generation (RAG) pipelines using frameworks like LangChain, LlamaIndex, OpenAI, and vector databases (FAISS, Pinecone, etc.).
Develop NLP solutions (NER, sentiment analysis, summarization, question answering, semantic search) and maintain scalable ML pipelines with performance monitoring and model optimization.
4-7 years of hands-on experience in Data Science, Machine Learning, or AI roles.
Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Mathematics, or related field.
Strong proficiency in Python and experience with LLM frameworks (LangChain, LlamaIndex, OpenAI, Azure OpenAI).
Experience with NLP libraries (Hugging Face Transformers, spaCy, NLTK), Deep Learning frameworks (PyTorch or TensorFlow), vector databases, and cloud platforms (AWS, Azure, or GCP).
Experienced in deploying large-scale AI/ML solutions in production with expertise in prompt engineering, hallucination mitigation, and RAG.
Comfortable working cross-functionally with engineering, product, and business teams to deliver AI solutions.
Familiar with building scalable data pipelines, REST APIs (FastAPI, Flask), and using Git, Docker, and CI/CD for deployment and version control.