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Mid-level ML/GenAI role in a metro with broad demand and common experience band increases applicant competition.
GenAI and LLM expertise is specialized but generally transferable across industries and product domains.
Explicit 4+ years and required hands-on GenAI/LLM and specific Python/LLM tooling enforce strict candidate filters.
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Develop and evaluate Large Language Model (LLM) powered solutions, including summarization, classification, extraction, and conversational assistants.
Design and implement prompt strategies, Retrieval-Augmented Generation (RAG) architectures, and fine-tuning or adapter approaches for GenAI applications.
Build Python-based prototypes and evaluation frameworks applying NLP and machine learning techniques like text classification, semantic search, clustering, and regression for LLM and GenAI use cases.
Minimum 4 years of experience in Data Science or Machine Learning with demonstrable GenAI/LLM project experience.
Strong proficiency in Python programming, including pandas, numpy, scikit-learn, and experience with LangChain/LlamaIndex or direct SDKs such as OpenAI, Vertex AI, or Hugging Face.
Hands-on experience with embeddings, vector search, Retrieval-Augmented Generation (RAG), prompt design, and LLM evaluation.
Solid understanding of statistics and experimentation methods including hypothesis testing, confidence intervals, power analysis, and A/B testing design.
Experienced in end-to-end design and deployment of LLM-powered solutions with expertise in advanced prompt engineering and RAG architectures.
Skilled in object-oriented and functional programming patterns tailored for scalable machine learning workflows.
Comfortable working on experimental frameworks requiring rigorous statistical validation and iterative model evaluation.