Back
Specialized ML and streaming skillset in Bangalore reduces applicant density despite company visibility.
ML and streaming expertise is moderately transferable across industries but demands domain-specific experience.
Multiple mandatory ML, streaming and production tooling requirements create strict technical screening for candidates.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Develop and optimize time series forecasting and NLP machine learning models using Python and ML frameworks to ensure robust and accurate predictions.
Integrate and process large-scale streaming data with Apache Kafka and Spark for real-time ML pipelines and feature engineering.
Evaluate and improve ML model performance using standard metrics and optimize training and deployment workflows in production environments.
Proficiency in machine learning techniques including supervised, unsupervised, deep, and reinforcement learning.
Strong experience in Python and ML libraries: Numpy, Pandas, Scikit-learn, TensorFlow, PyTorch, XGBoost, LightGBM.
Experience with time series analysis and forecasting methodologies.
Solid skills in Apache Spark and Kafka for distributed data processing and real-time streaming analytics.
Experienced in developing scalable ML solutions involving both time series forecasting and NLP applications.
Comfortable working with large-scale streaming data environments and real-time feature engineering using Spark and Kafka.
Capable of applying rigorous evaluation methods to ensure model reliability and production readiness.