I’m an MLOps Engineer who has built and scaled ML systems in fintech and enterprise setups — and I’ve also been through 300+ interviews along the way. From CI/CD automation and Kubernetes-based inference to drift-aware retraining pipelines and LLM deployment workflows, I’ve seen what actually works in production — and what only sounds good on paper. If you're: • Stuck preparing for DevOps/MLOps interviews • Trying to break into product companies • Confused about LLMOps vs MLOps • Or want honest resume feedback I’m here to share real experience, not textbook theory.