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Mid-level ML title, 3+ years range, and metro location increase applicant density.
Core ML and LLM skills are broadly transferable across industries with low domain lock-in.
Explicit 3+ years plus mandatory Python/ML frameworks and model experience enforce moderate screening.
Design, develop, train, and optimize machine learning and deep learning models for business challenges and improved customer outcomes.
Build and enhance Generative AI solutions including large language model (LLM) applications and retrieval-augmented generation (RAG) systems.
Collaborate with cross-functional teams (Marketing, Sales, Product, Engineering) to translate business problems into AI-driven solutions and conduct model experimentation and performance evaluation.
At least 3 years of experience in developing, implementing, and optimizing machine learning and deep learning models.
Proficiency in Python and SQL; hands-on experience with machine learning frameworks such as Scikit-learn, TensorFlow, and/or PyTorch.
Bachelor's or Master's degree in Computer Science, Data Science, Artificial Intelligence, Statistics, or related quantitative field.
Experience with Large Language Models (LLMs), RAG, LangChain, and LlamaIndex is highly preferred; exposure to cloud environments like Google Cloud Platform (GCP) is a plus.
Strong technical proficiency across the entire data science lifecycle including feature engineering, statistical modeling, experimentation, and model optimization.
Experience in applying advanced AI technologies such as Generative AI and LLM-based solutions in a business context.
Ability to work cross-functionally with marketing, sales, product, and engineering teams to deliver predictive analytics and AI solutions that generate measurable business value.