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Popular ML/AI role with LLM demand but senior non-metro posting limits applicant density.
Medium because core ML/MLOps skills transfer across industries, though utility/smart-city domain knowledge helps.
High due to explicit 8-year requirement and extensive mandatory ML, DL, MLOps, and production skills.
Develop and deploy AI/ML models addressing utility and smart city business challenges, including predictive analytics and forecasting.
Build, validate, and optimize machine learning and deep learning models for production, implementing MLOps best practices such as model monitoring and retraining.
Collaborate with business and product teams to translate requirements into data-driven solutions, develop dashboards and reports, and mentor team members.
8 years of work experience in data science or related field.
Advanced proficiency in Python (including Pandas, NumPy, SciPy, Scikit-learn) and SQL for large-scale data analysis.
Strong expertise in machine learning techniques including regression, classification, clustering, ensemble methods (Random Forest, XGBoost, LightGBM), and deep learning frameworks (PyTorch or TensorFlow).
Experience with production data science and MLOps tools/workflows such as MLflow, Kubeflow, Docker, and CI/CD for model deployment and monitoring.
Experienced in end-to-end data science projects including feature engineering, predictive modeling, and production deployment within utility or smart city domains.
Capable of leading data science initiatives, mentoring junior team members, and collaborating effectively with cross-functional business teams.
Familiar with integrating generative AI/LLM techniques and big data/cloud platforms (AWS SageMaker, Azure ML, Spark) to scale AI/ML solutions.