Machine Learning Engineer
Innovating Tomorrow, Today
Machine Learning Engineer and dedicated self-learner with hands-on experience in building and deploying ML models for real-world applications. Skilled in Python, TensorFlow, PyTorch, and data pipelines. Proven ability to improve model performance and collaborate cross-functionally to deliver production-ready solutions. Passionate about optimizing algorithms and applying AI to solve business problems.
Creative & Professional
Designed and trained a customer churn prediction model using XGBoost, improving prediction accuracy by 18% over baseline.
Built automated ETL pipelines with Pandas and SQL, reducing preprocessing time by 30%.
Deployed a real-time recommendation system via Flask API on AWS EC2, handling 50k+ requests/day.
Conducted A/B testing to validate model improvements, leading to 12% increase in user engagement.
Collaborated with data engineers and product managers to translate business problems into ML solutions.
Implemented an image classifier using PyTorch trained on the CIFAR-10 dataset with 92% accuracy. Applied data augmentation techniques and transfer learning with pre-trained models.
Implemented a hybrid recommendation system combining collaborative filtering and content-based methods. Trained on a movie ratings dataset and deployed as a REST API using Flask and Docker.
Invertis University
GPA: 74
INEVERTIS UNIVERSITY
GPA: 74