Key Responsibilities :
- Conduct feature engineering, data analysis, and data exploration to extract valuable insights.
- Develop and optimize Machine Learning models to achieve high accuracy and performance.
- Design and implement Deep Learning models, including Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Reinforcement Learning techniques.
- Handle real-time imbalanced datasets and apply appropriate techniques to improve model fairness and robustness.
- Deploy models in production environments and ensure continuous monitoring, improvement, and updates based on feedback.
- Collaborate with cross-functional teams to align ML solutions with business goals.
- Utilize fundamental statistical knowledge and mathematical principles to ensure the reliability of models.
- Bring in the latest advancements in ML and AI to drive innovation.
Requirements :
- 4-5 years of hands-on experience in Machine Learning and Deep Learning.
- Strong expertise in feature engineering, data exploration, and data preprocessing.
- Experience with imbalanced datasets and techniques to improve model generalization.
- Proficiency in Python, TensorFlow, Scikit-learn, and other ML frameworks.
- Strong mathematical and statistical knowledge with problem-solving skills.
- Ability to optimize models for high accuracy and performance in real-world scenarios.Preferred Qualifications :
- Experience with Big Data technologies (Hadoop, Spark, etc.)
- Familiarity with containerization and orchestration tools (Docker, Kubernetes).
- Experience in automating ML pipelines with MLOps practices.
- Experience in model deployment using cloud platforms (AWS, GCP, Azure) or MLOps tools.
Keyskills: Machine Learning MLOps Hadoop Big Data Neural Networks Spark Scikit-Learn Deep Learning Python TensorFlow