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
Skills Required
Machine Learning, Deep Learning, Python, Tensorflow