The interview process started with promising technical depth but ultimately resulted in a complete waste of time, money, and energy due to an entirely unprofessional face-to-face managerial phase in Bangalore. Round 1 & 2 (Technical): These initial technical sessions were highly professional and well-structured. One focused on system design and LLM concepts, while the second round with an Architect involved practical problem-solving and a thorough review of production AI projects. Round 3 (F2F Managerial): This was a highly counterproductive experience that showed a severe lack of respect for a candidate's time. Despite traveling for a scheduled 11:00 AM to 11:45 AM slot, the manager made me wait under the pretext of being "too busy," causing the interview to start late. Throughout the session, he demonstrated a complete lack of interest and was not actively listening, culminating in him physically leaving the room mid-interview to conduct a separate session. The technical questions he asked lacked basic context (e.g., asking "What is the output of Databricks?"). Furthermore, the conversation devolved into a circular argument because he insisted on treating Precision as a mandatory, textbook checkbox, displaying a fundamental disconnect with how metrics are strategically selected in actual production AI environments. He concluded by declaring his skepticism and adding an unplanned round. Round 4 (Virtual Technical): This unscheduled follow-up round was conducted by a Data Engineer. It consisted of several highly vague questions alongside standard data engineering pipeline testing and SQL queries. Pros The initial technical interviewers and Architects were highly competent, professional, and understood the modern AI stack. Cons A total lack of organizational professionalism and respect for the candidate. Forcing a candidate to travel for a face-to-face interview only to have the manager be late, completely distracted, and physically walk out of the room is unacceptable. Adding unplanned technical hurdles because a manager lacks the production domain knowledge to properly evaluate a senior candidate shows an incredibly broken internal process. Advice to Management Do not waste the time and resources of candidates by bringing them in for face-to-face interviews if your managers are too busy to remain in the room and focus. Managerial rounds should focus on team culture and leadership, not ad-hoc, textbook gatekeeping by individuals who do not understand production-level AI engineering choices.