Day-to-Day Responsibilities
Turn a business problem into an AI approach, such as a classifier, a recommendation system or a language model feature.
Collect, clean and prepare data, and check it for quality, gaps and bias before training any model.
Train, test and tune machine learning and deep learning models, and track each experiment's results.
Build APIs and data pipelines so that models run reliably inside apps and websites.
Work with generative AI tools: prompt and fine-tune large language models, add retrieval (RAG) and safety guardrails.
Monitor models in production for accuracy drop, speed and cost, and retrain them when needed.
Document decisions, explain results to product managers, and follow data privacy and responsible-AI rules.
