Scitech Industries

I appeared for the SciTech Industries interview process, which primarily focused on integrating machine learning models into full-stack web applications. The discussion centered on system architecture, backend design, API communication, and deployment strategies for ML-powered applications. The interviewer began by asking where the machine learning component should run, discussing the advantages and trade-offs of running inference on the frontend, backend, or as a separate microservice. This was followed by questions on how data is sent from a React frontend to an ML model, where I explained the use of REST APIs to send JSON payloads from the client to the backend, which then communicates with the model service. The discussion then moved to backend implementation. I was asked what format the model output is returned in, and I explained how predictions are typically returned as structured JSON responses containing labels, confidence scores, or other relevant outputs. The interviewer also asked how predictions are stored in MongoDB, where I discussed designing appropriate schemas to persist inputs, predictions, timestamps, and user-related information when required. The interview also covered performance optimization. I explained how to handle slow model inference in an API, discussing approaches such as asynchronous processing, caching, background job queues, and batching requests when applicable. A follow-up question focused on avoiding blocking the Node.js event loop, where I described using asynchronous operations, worker threads, or separate inference services for computationally intensive tasks. Finally, I was asked how to deploy a MERN application with machine learning inference. I explained a typical architecture where the React frontend, Node.js backend, and ML inference service are deployed independently, with the backend acting as the communication layer between the frontend and the model. Overall, the interview emphasized practical system design, backend architecture, and the integration of machine learning into production-ready web applications.