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Metro Bangalore and mid-level ML role increases applicants, but niche GCP, KubeFlow, BentoML stack reduces density.
Core ML engineering and cloud deployment skills are broadly transferable across industries despite some forecasting specifics.
Explicit 2–4 years plus many mandatory ML, cloud, deployment tools indicates strict technical filters.
Design, develop, and deploy advanced machine learning models using classic and deep learning methods to solve complex business problems.
Conduct exploratory data analysis and statistical methods to extract insights and support model accuracy and forecasting.
Implement, optimize, and manage AI/ML models on Google Cloud Platform using tools like KubeFlow and BentoML to ensure scalable and reliable production deployments.
2 to 4 years of hands-on experience in machine learning, data science, or AI engineering roles.
Proficiency in Python and SQL; experience with TensorFlow, PyTorch, Sci-Kit Learn or similar ML frameworks.
Experience with statistical analysis methods including hypothesis testing, regression, classification techniques, and forecasting (exponential smoothing, ARIMA, ARIMAX).
Bachelor's degree in Computer Science, Data Science, Statistics, Mathematics, or closely related field.
Experienced in end-to-end ML model development and deployment on Google Cloud Platform in production environments.
Familiar with advanced statistical and probabilistic modeling approaches, including probabilistic graph models and performance monitoring frameworks.
Capable of collaborating with cross-functional teams to integrate AI solutions with business workflows, ensuring measurable impact and operational robustness.