A typical day would involve:
Researching and assessing AI use case feasibility across Machine Learning and Generative AI (technical viability, data readiness, and value potential)Exploring, cleaning, and transforming data to support proof of concept (PoC) experimentsBuilding and experimenting with ML and GenAI models (e.g., prompt engineering, embeddings, fine‑tuning, classical ML)Participating in scrum ceremonies (stand‑ups, sprint planning, demos) alongside managers and senior engineersSupporting model deployment pipelines using Azure and Databricks, including CI/CD concepts and MLOps best practicesDocumenting findings, tradeoffs, and recommendations to support leadership decision-making You are:
Results-Oriented – You balance experimentation with delivering tangible outcomesCritical Thinker - You evaluates feasibility, trade‑offs, and limitations, not just...