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.

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.

Experience with Big Data technologies (e.g., Hadoop, Spark).
Familiarity with containerization and orchestration tools (e.g., Docker, Kubernetes).
Experience in automating ML pipelines with MLOps practices.
Experience in model deployment using cloud platforms (AWS, GCP, Azure) or MLOps tools.

Machine Learning Engineer - Python/Hadoop