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Which is more crucial today: AI or ML Engineer? I've explained it in a simple way below. 𝗔𝗜 𝗘𝗡𝗚𝗜𝗡𝗘𝗘𝗥 • Builds complete AI-powered products end to end • Brings AI into real-world apps and business workflows • Works on inference, APIs, agents, and orchestration • Uses pre-trained models and adapts them for production • Focuses on latency, scalability, and user experience • Connects models with databases, tools, and external APIs • Thinks more like a product + systems engineer • Ships AI features that users actually interact with • Often works closely with frontend, backend, and product teams 𝗠𝗟 𝗘𝗡𝗚𝗜𝗡𝗘𝗘𝗥 • Focuses on training, fine-tuning, and improving models • Designs custom model architectures and ML pipelines • Handles data cleaning, preprocessing, and feature engineering • Tunes hyperparameters and runs structured experiments • Tracks model performance, metrics, and accuracy • Works deeply with datasets, labels, and evaluation methods • Thinks more like a research + optimization engineer • Improves how smart and accurate the model itself is • Works closely with large datasets and statistical methods What actually changed? 𝗧𝗵𝗲 𝗚𝗲𝗻𝗔𝗜 𝗹𝗮𝘆𝗲𝗿 • LLM & generative AI systems • Prompt and context engineering • RAG pipelines (retrieval + generation) • Fine-tuning foundation models for domain use • Tool calling and function execution • Multi-step LLM workflows and agents • AI observability (hallucinations, drift, latency, cost) • Human-in-the-loop systems and feedback cycles This layer didn’t replace ML. It 𝘀𝗵𝗶𝗳𝘁𝗲𝗱 𝘄𝗵𝗲𝗿𝗲 𝗺𝗼𝘀𝘁 𝘃𝗮𝗹𝘂𝗲 𝗶𝘀 𝗰𝗿𝗲𝗮𝘁𝗲𝗱. 𝗪𝗵𝗶𝗰𝗵 𝗿𝗼𝗹𝗲 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗳𝗶𝘁𝘀 𝘆𝗼𝘂? • If you like 𝘀𝘆𝘀𝘁𝗲𝗺𝘀, 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝘀, 𝗮𝗻𝗱 𝘂𝘀𝗲𝗿𝘀 → AI Engineer • If you like 𝗱𝗮𝘁𝗮, 𝗺𝗮𝘁𝗵, 𝗮𝗻𝗱 𝗲𝘅𝗽𝗲𝗿𝗶𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 → ML Engineer • If you enjoy 𝘀𝗵𝗶𝗽𝗽𝗶𝗻𝗴 𝗳𝗮𝘀𝘁 𝗮𝗻𝗱 𝗶𝘁𝗲𝗿𝗮𝘁𝗶𝗻𝗴 𝗶𝗻 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 → AI Engineer • If you enjoy 𝗱𝗲𝗲𝗽 𝗼𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗮𝗻𝗱 𝗺𝗼𝗱𝗲𝗹 𝗿𝗶𝗴𝗼𝗿 → ML Engineer Today, most companies aren’t blocked by model quality. They’re blocked by: • Integration into real workflows • Reliability in production • Cost and latency at scale • Usable, trustworthy user experiences That’s why many ML roles are evolving toward AI Engineering. ✅ Repost for others in your network so they can understand this.