Job Description

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) experiments

  • Building 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 engineers

  • Supporting model deployment pipelines using Azure and Databricks, including CI/CD concepts and MLOps best practices

  • Documenting findings, tradeoffs, and recommendations to support leadership decision-making

    • You are:

  • Results-Oriented – You balance experimentation with delivering tangible outcomes

  • Critical Thinker - You evaluates feasibility, trade‑offs, and limitations, not just...

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