The position was advertised as Senior Machine Learning Engineer. After the second interview, I received a call saying the role was actually much more MLOps-focused and asking whether I wanted to do ML or MLOps. I replied that I had applied because it was advertised as an MLE position, not an MLOps one. They then disappeared for three weeks to discuss whether the role could actually include ML engineering work. After those three weeks, they told me it would, so I agreed to continue. Over the next month, I completed four more interview rounds covering ML engineering topics, including ML theory and ML system design—not MLOps. Then, in the final interview with the VP, I was asked the exact same question again: ML or MLOps? I said I can do both, but if I have a preference, I'd choose ML engineering. The rejection reason was that my expectations and the role were "not aligned." Apparently, it took two months, five interviews, and an internal three-week discussion about this exact topic to reach the same conclusion they had already identified after the first interview. To make matters worse, questions about fundamental requirements such as on-prem experience only came up in the final round. The process gave the impression that the company itself hadn't decided what the role actually was. If the job is primarily MLOps, advertise it as MLOps. Don't advertise an MLE role, spend months interviewing candidates on ML engineering, and then reject them for preferring ML engineering.