Solid understanding of ML fundamentals : data splits, cross-validation, overfitting, regularization, and key metrics (accuracy, F1, ROC-AUC, RMSE).
Experience using scikit-learn and at least one of PyTorch or TensorFlow.
Familiarity with Jupyter / Colab environments, Git version control, and virtual environment packaging.
Experience handling data formats (CSV, Parquet, JSON) and performing basic data quality checks.
Clear written communication skills and a strong habit of experiment documentation.
Desirable Skills :
Knowledge of NLP basics : tokenization, TF-IDF, embeddings, and Hugging Face tools.
Exposure to simple MLOps : Docker, FastAPI / Flask for model inference, and basic CI practices.
Familiarity with cloud platforms (AWS, Azure, GCP) and object storage solutions (S3, Blob).
Experience with experiment tracking tools (MLflow, Weights & Biases) and basic dashboarding.
Understanding of prompt engineering and Retrieval-Augmented Generation (RAG) for LLM tasks.
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Ai Ml Engineer • Kaloor, KL, India
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