Candidates must possess strong engineering rigor, including DevOps, Git, and Cloud fundamentals. They will write production code. The pipeline relies heavily on AWS and utilizes Jenkins for CI/CD workflows, though experience with alternative tools (like GitHub Actions) is acceptable.AI Productivity Tools.
Candidates should actively use and understand AI coding companions such as GitHub Copilot or Cursor (referred to on the call as "core code/code power"). Elite utilization of these tools is a major differentiator.The Experimentation Gap: Traditional Machine Learning backgrounds (hyperparameter tuning, classic data cleaning, feature engineering) are highly respected for establishing an experimental mindset, but Jesse warned that this skill set is not directly translatable to GenAI. Candidates must have hands-on experience in Prompt Engineering, Context Engineering, and constructing frameworks to evaluate hallucinations, response friendliness, and automated validation metrics.
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