Description
/Preferred Qualifications
Required skills/Competencies
Programming LanguagesStrong in Python, data structures, and algorithms.Hands-on with NumPy, Pandas, Scikit-learn for ML prototyping.Machine Learning FrameworksUnderstanding of supervised/unsupervised learning, regularization, feature engineering, model selection, cross-validation, ensemble methods (XGBoost, LightGBM).Deep Learning TechniquesProficiency with PyTorch or TensorFlow/KerasKnowledge of CNNs, RNNs, LSTMs, Transformers, Attention mechanisms.Familiarity with optimization (Adam, SGD), dropout, batch norm.LLMs & RAGHugging Face Transformers (tokenizers, embeddings, model fine-tuning).Vector databases (Milvus, FAISS, Pinecone, ElasticSearch).Prompt engineering, function/tool calling, JSON schema outputs.Data & ToolsSQL fundamentals; exposure to data wrangling and pipelines.Git/GitHub, Jupyter, basic Docker.What are we looking for?