Strong proficiency in Python. Practical experience deploying machine learning solutions using tools such as Flask FastAPI and Docker Hands-on familiarity with leading ML libraries: scikit-learn TensorFlow PyTorch or XGBoost. Knowledge of big data tools and frameworks (e.g. Spark Hadoop) for working with large-scale datasets. Familiarity with cloud platforms (AWS/ GCP). Solid understanding of core machine learning principles including training validation data preprocessing and feature engineering. Proven experience in ML engineering deploying maintaining and monitoring models in production environments.
**Preferred** Hands-on experience with MLOps frameworks (MLflow Kubeflow or SageMaker). Familiarity with optimizing model performance for accuracy efficiency and scalability in production settings. Experience with both SQL and NoSQL databases for effective data storage extraction and integration into ML pipelines. Strong communication skills and ability to learn fast.