Available to hire
I am a results-driven AI Engineer with 3+ years of experience in designing, developing, and deploying AI and machine learning solutions across diverse domains. I have deep expertise in deep learning, NLP, computer vision, and reinforcement learning, and I am proficient in Python, TensorFlow, PyTorch, and scikit-learn. I enjoy building end-to-end AI systems—from data preprocessing and model architecture design to model optimization and MLOps deployment. I have leveraged cloud platforms like AWS and GCP to train scalable models and enable low-latency inference.\n\nI am passionate about applying AI to solve real-world problems, enhance decision-making, and deliver measurable business value. I thrive in cross-functional teams, follow SDLC and Agile methodologies, and have experience in creating production-ready pipelines, monitoring, and automated retraining to maintain model performance.
Skills
Work Experience
AI Engineer at Anblicks
February 1, 2025 - PresentDeveloped a hybrid recommendation system integrating collaborative filtering, content-based filtering, deep autoencoder networks, and large language models, driving a 15% increase in sales and significantly improving customer engagement. Architected end-to-end AI pipelines including data ingestion, cleaning, and feature engineering with Python (Pandas, NumPy) and SQL. Engineered and optimized deep learning models (autoencoders) and LLM-based personalization layers, achieving 92%+ accuracy in user preference prediction through cross-validation and Bayesian hyperparameter tuning. Automated model training and serverless deployment with AWS SageMaker, Lambda, and Amazon Redshift, enabling scalable, low-latency inference in production. Built CI/CD pipelines with Docker and GitHub Actions for efficient model updates, and integrated AWS CloudWatch and Prometheus for real-time monitoring and automated retraining.
Data Scientist Intern at Zillow
December 1, 2024 - October 8, 2025Collected and curated dataset of over 10,000 properties, integrating features such as location, size, and amenities to improve model accuracy by 15%. Preprocessed and cleaned the dataset, handling missing values through imputation and performing feature engineering that increased predictive power by 20%, visualized relationships with Matplotlib and Seaborn to inform model selection and feature importance, leading to a 30% reduction in model training time. Split the dataset into training and validation sets, achieving a training accuracy of 85% on initial models. Trained multiple algorithms (Linear Regression, Random Forest, Gradient Boosting, SVR) and evaluated with RMSE and R², resulting in a model ensemble that improved prediction accuracy by 25%.
Machine Learning Engineer at Magna Info Tech
December 1, 2022 - October 8, 2025Developed a predictive model using ML (Linear Regression, Random Forest, XGBoost, LightGBM) and DL (MLPs, RNNs) on a dataset of 10,000+ features to forecast property sale prices. Executed data preprocessing, including handling 5% missing data via imputation, normalization, and NLP techniques (tokenization, stemming, lemmatization, sentiment analysis) on real estate text data from Zillow and Realtor.com. Designed and implemented scalable ETL pipelines to extract, transform, and load heterogeneous real estate data from multiple sources into structured formats. Applied feature selection methods like Recursive Feature Elimination (RFE) and PCA to optimize model efficiency and performance. Conducted A/B testing and cross-validation to evaluate and compare algorithms, driving data-driven decisions for the best-performing predictive solution.
AI Engineer at Anblicks
February 1, 2025 - PresentDeveloped a hybrid recommendation system integrating collaborative filtering, content-based filtering, deep autoencoder networks, and large language models, driving a 15% increase in sales and significantly improving customer engagement. Architected end-to-end AI pipeline including data ingestion, cleaning, and feature engineering using Python (Pandas, NumPy) and SQL. Engineered and optimized deep learning models (autoencoders) and LLM-based personalization layers, achieving 92%+ accuracy in user preference prediction through cross-validation and Bayesian hyperparameter tuning. Automated model training and serverless deployment with AWS SageMaker, Lambda, and Amazon Redshift, enabling scalable, low-latency inference in production environments. Built CI/CD pipelines with Docker and GitHub Actions for efficient model updates, and integrated AWS CloudWatch and Prometheus for real-time monitoring and automated retraining.
Data Scientist Intern at Zillow
December 1, 2024 - October 8, 2025Collected and curated a dataset of over 10,000 properties, integrating features such as location, size, and amenities to improve model accuracy by 15%. Preprocessed and cleaned the dataset, effectively handling missing values through imputation techniques and performing feature engineering that increased predictive power by 20% using Pandas. Visualized data relationships and patterns with Matplotlib and Seaborn, identifying key insights that informed model selection and feature importance, leading to a 30% reduction in model training time. Trained multiple machine learning algorithms (Linear Regression, Random Forest, Gradient Boosting, SVR) and evaluated performance using RMSE and R², resulting in a model ensemble that improved prediction accuracy by 25%. Split the dataset into training and validation sets, achieving an 85% training accuracy on initial models.
Machine Learning Engineer at Magna Info Tech
December 1, 2022 - October 8, 2025Developed a predictive model using machine learning (Linear Regression, Random Forest, XGBoost, LightGBM) and deep learning (MLPs, RNNs) frameworks with TensorFlow and PyTorch, using a dataset of 10,000+ features to forecast property sale prices. Executed data preprocessing, including handling 5% missing data via imputation, normalization, and NLP techniques on real estate text data from Zillow and Realtor.com. Designed and implemented scalable ETL pipelines to extract, transform, and load heterogeneous real estate data from multiple sources into structured formats, ensuring data integrity and seamless model integration. Applied feature selection methods like Recursive Feature Elimination (RFE) and PCA to optimize model efficiency and performance. Conducted A/B testing and cross-validation to evaluate and compare algorithms and configurations.
Education
Master of Science in Data Science at University of Central Missouri
January 1, 2023 - December 1, 2024Bachelor's of Technology in Computer Science at St. Mary's College
August 1, 2018 - August 1, 2021Master of Science in Data Science at University of Central Missouri
January 1, 2023 - December 1, 2024Bachelor of Technology in Computer Science at St. Mary's College
August 1, 2018 - August 1, 2021Qualifications
Industry Experience
Software & Internet, Professional Services
Skills
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