ai/ml data engineer.

• Programming & Foundations
• Strong in Python, data structures, and algorithms.
• Hands-on with NumPy, Pandas, Scikit-learn for ML prototyping.
• Machine Learning
• Understanding of supervised/unsupervised learning, regularization, feature engineering, model selection, cross-validation, ensemble methods (XGBoost, LightGBM).
• Deep Learning
• Proficiency with PyTorch (preferred) or TensorFlow/Keras.
• Knowledge of CNNs, RNNs, LSTMs, Transformers, Attention mechanisms.
• Familiarity with optimization (Adam, SGD), dropout, batch norm.
• LLMs & RAG
• Hugging Face Transformers (tokenizers, embeddings, model fine-tuning).
• Vector databases (Milvus, FAISS, Pinecone, ElasticSearch).
• Prompt engineering, function/tool calling, JSON schema outputs.
• Data & Tools
• SQL fundamentals; exposure to data wrangling and pipelines.
• Git/GitHub, Jupyter, basic Docker.

experience

10