11/02/2026 updated
100 % available
Machine Learning Engineer
Borivali(East),Mumbai, India
Only remote
B.E. in Computer EngineeringComputer VisionMachine LearningMongoDBOpenCVTensorFlowAzure Machine LearningFeature EngineeringRandom ForestScikit LearnMachine Learning Operations
Machine Learning Model Development
Hands-on experience building ML models including KNN, Random Forest, and recommendation systems. Application of these models in real-world scenarios such as bacterial infection diagnosis and heart stroke detection with high accuracy results.
Python & ML Frameworks
Proficiency in Python along with key ML libraries such as Scikit-learn, TensorFlow, and OpenCV. Usage of these tools to develop, train, and evaluate machine learning models across multiple projects.
Data Preprocessing & Feature Engineering
Strong skills in data preprocessing and feature engineering, including handling clinical and movie metadata datasets. Application of text vectorization, TF-IDF, and Cosine Similarity algorithms to transform raw data into model-ready formats.
Recommendation Systems
Development of a content-based movie recommendation system resulting in a 50% increase in user engagement, using text vectorization and pairwise similarity algorithms.
Database Management (MongoDB & MySQL)
Experience storing and querying datasets using MongoDB and MySQL, ensuring efficient data retrieval for machine learning model training.
Cloud Platforms & Supabase
Management of data collection and storage using Supabase, ensuring clean and query-ready datasets for model training in cloud environments.
OpenCV
Familiarity with OpenCV as part of the ML toolkit, applied in computer vision-related tasks within machine learning projects.
Hackathon & Team Collaboration
Proven ability to collaborate in fast-paced hackathon teams, lead technical projects, and deliver functional prototypes under tight deadlines, as demonstrated at the IIT Madras Hackathon.
Hands-on experience building ML models including KNN, Random Forest, and recommendation systems. Application of these models in real-world scenarios such as bacterial infection diagnosis and heart stroke detection with high accuracy results.
Python & ML Frameworks
Proficiency in Python along with key ML libraries such as Scikit-learn, TensorFlow, and OpenCV. Usage of these tools to develop, train, and evaluate machine learning models across multiple projects.
Data Preprocessing & Feature Engineering
Strong skills in data preprocessing and feature engineering, including handling clinical and movie metadata datasets. Application of text vectorization, TF-IDF, and Cosine Similarity algorithms to transform raw data into model-ready formats.
Recommendation Systems
Development of a content-based movie recommendation system resulting in a 50% increase in user engagement, using text vectorization and pairwise similarity algorithms.
Database Management (MongoDB & MySQL)
Experience storing and querying datasets using MongoDB and MySQL, ensuring efficient data retrieval for machine learning model training.
Cloud Platforms & Supabase
Management of data collection and storage using Supabase, ensuring clean and query-ready datasets for model training in cloud environments.
OpenCV
Familiarity with OpenCV as part of the ML toolkit, applied in computer vision-related tasks within machine learning projects.
Hackathon & Team Collaboration
Proven ability to collaborate in fast-paced hackathon teams, lead technical projects, and deliver functional prototypes under tight deadlines, as demonstrated at the IIT Madras Hackathon.
Project history
Pursuing a B.E. in Computer Engineering at St. Francis Institute of Technology, Mumbai, India. Current CGPA: 8.86.
Developed a content-based movie recommendation system to strengthen hands-on machine learning skills. Applied text vectorization and TF-IDF to transform movie metadata into numerical feature vectors. Computed pairwise similarity using the Cosine Similarity algorithm to generate personalized recommendations. Used MongoDB and MySQL to store and query movie datasets efficiently. Resulted in a 50% increase in user engagement.
Built a heart stroke prediction model using a Random Forest classifier trained on patient health records. Preprocessed clinical data and engineered features such as age, blood pressure, and cholesterol to improve model performance. Evaluated the model to flag high-risk patients with 90% accuracy, demonstrating a practical early-detection use case for ML in healthcare.