Artificial Engineer

1. Programming Languages o Python (primary for AI/ML): TensorFlow, PyTorch, Scikit-learn, Pandas, NumPy o R (for statistical modeling) o Optional: Java, C++, or Julia for performance-critical tasks 2. Machine Learning & AI o Supervised & Unsupervised Learning (classification, regression, clustering) o Deep Learning (CNNs, RNNs, Transformers) o Natural Language Processing (NLP): Tokenization, embeddings, LLM fine-tuning o Generative AI: Prompt engineering, model deployment 3. Mathematics & Statistics o Linear algebra, calculus, probability, and optimization o Statistical inference and hypothesis testing 4. Data Engineering & Preprocessing o Data cleaning, feature engineering, and handling large datasets o Familiarity with PySpark or Spark MLlib for distributed ML 5. Model Deployment & MLOps o Containerization (Docker, Kubernetes) o CI/CD for ML pipelines o Model monitoring and retraining strategies