3-5+ years of experience building, deploying, and operating machine learning systems in production. Strong programming skills in Python, Java, Go, or similar languages, with solid software engineering fundamentals. ML Fundamentals: a strong grasp of algorithms, from classic statistical learning (XGBoost, Random Forests, regressions) to DL architectures (Transformers, CNNs, GNNs). Hands-on experience with modern ML frameworks (e.g., PyTorch, TensorFlow). Experience designing scalable ML pipelines, data processing systems, and model serving infrastructure. Ability to work cross-functionally and translate ambiguous product or business problems into technical solutions. Experience improving measurable metrics through applied machine learning.