Responsible AI Policy for a HR-Tech Scale-up

Between a research notebook that proves something might work and a system that serves predictions to millions of users sits the machine learning engineer. Their job is to make models real — reliable, fast, observable, and easy to roll back when something goes sideways. Expect to write production Python, design training pipelines in PyTorch, wire up CI/CD that catches a regression before a customer does, and argue thoughtfully about latency budgets. Students often arrive from data science and discover they enjoy the engineering half more than they expected. Growing into the role means caring about model performance and software craft equally, and treating offline evaluation metrics with the same seriousness as accuracy.

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