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