Key Responsibilities Conduct feature engineering, data analysis, and data exploration to extract valuable insights
Develop and optimize machine learning models for high accuracy and performance
Design and implement deep learning models such as ANN, CNN, and reinforcement learning techniques
Handle real-time imbalanced datasets and apply techniques to improve model fairness and robustness
Deploy models in production environments and ensure continuous monitoring and updates
Collaborate with cross-functional teams to align ML solutions with business goals
Apply statistical and mathematical principles to ensure model reliability
Integrate latest advancements in ML/AI to drive innovation
Requirements 4–5 years of hands-on experience in machine learning and deep learning
Expertise in feature engineering, data exploration, and preprocessing
Experience working with imbalanced datasets and improving model generalization
Proficiency in Python, TensorFlow, Scikit-learn, and other ML libraries
Strong foundation in mathematics, statistics, and problem-solving
Ability to optimize models for real-world performance
Preferred Qualifications Experience with Big Data technologies like Hadoop and Spark
Familiarity with Docker, Kubernetes, and containerization tools
Knowledge of MLOps practices and automating ML pipelines
Experience deploying models on cloud platforms such as AWS, GCP, or Azure
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Develop and optimize machine learning models for high accuracy and performance
Design and implement deep learning models such as ANN, CNN, and reinforcement learning techniques
Handle real-time imbalanced datasets and apply techniques to improve model fairness and robustness
Deploy models in production environments and ensure continuous monitoring and updates
Collaborate with cross-functional teams to align ML solutions with business goals
Apply statistical and mathematical principles to ensure model reliability
Integrate latest advancements in ML/AI to drive innovation
Requirements 4–5 years of hands-on experience in machine learning and deep learning
Expertise in feature engineering, data exploration, and preprocessing
Experience working with imbalanced datasets and improving model generalization
Proficiency in Python, TensorFlow, Scikit-learn, and other ML libraries
Strong foundation in mathematics, statistics, and problem-solving
Ability to optimize models for real-world performance
Preferred Qualifications Experience with Big Data technologies like Hadoop and Spark
Familiarity with Docker, Kubernetes, and containerization tools
Knowledge of MLOps practices and automating ML pipelines
Experience deploying models on cloud platforms such as AWS, GCP, or Azure
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