Machine Learning Quality Assurance

SAKETH JAGGAIAHGARI

Ohio, United States LinkedIn: Saketh Jaggaiahgari ******************@*****.*** +1-513-***-**** EDUCATION

Master of Engineering in Computer Science- University of Cincinnati, Ohio (August 2023-April 2025) Coursework: Advanced Algorithms, Operating Systems, Cloud Computing, Software Testing and Quality Assurance. Bachelor of Technology in Computer Science and Engineering – Gandhi Institute of Technology and Management

(Deemed to be University), Hyderabad, India (June 2019 – April 2023) Coursework: Natural Language Processing, Neural Networks and Deep Learning, Machine Learning, Software Engineering, OOPS with Java, Programming with C

INTERNSHIP EXPERIENCE

Synergy Software Solutions, OH (Jan 2025 – Apr 2025)

Developed real-time analytics & BI platform for customer/transaction insights using XGBoost & Python, achieving 25% improvement in churn prediction accuracy.

Built and deployed REST APIs in Java (Spring Boot) for model inference and dashboard access.

Automated workflows using AWS Lambda, hosted services on EC2, and managed data on S3.

Created dashboards in Tableau and ReactJS for actionable business insights, boosting stakeholder engagement by 30%. Machine Learning Research Intern - Centella AI Therapeutics (September 2022 – July 2023)

Developed a Generative Chemistry application using Graph Neural Networks, reducing molecule generation time by 30% and increasing variety by 25%, enhancing drug design efficiency.

Conducted domain analysis with 85% accuracy and applied frequency analysis on 200+ molecular descriptors, improving predictive insights by 20% and reducing data discrepancies by 40% through API-driven validation. Software Developer Intern - Kognitive LLC (May 2022 – July 2022)

Redesigned the website using ReactJS, boosting user engagement by 25% and reducing bounce rates by 10%, leading to higher customer satisfaction and conversions.

Enhanced website performance and scalability by 15% with AWS services (S3, Lambda, DynamoDB), and refined event management systems that increased awareness of client’s activities by 35%. PROJECTS

Hybrid Recommender System (Guided Project) Python, TensorFlow, AWS SageMaker (January 2025)

Composed a hybrid recommender system combining Restricted Boltzmann Machines (RBM) and Content-Based Filtering, improving accuracy from 1.1891 RMSE to 1.1045 RMSE.

Leveraged Amazon DSSTNE to process large-scale sparse user-item interactions, improving training efficiency by 35%.

Introduced matrix factorization and ensemble techniques to optimize rating predictions, reducing prediction bias by 12%. Customer Feedback Sentiment Analyzer .NET, Tensorflow (October 2024)

Developed a .NET Core application to analyze customer feedback from surveys, using a Tensorflow sentiment analysis model to classify responses as positive, neutral, or negative with 85% accuracy.

Built an ASP.NET Core Web API for real-time feedback processing and integrated a Blazor dashboard to visualize sentiment trends and insights.

Implemented unit testing and performance optimization to ensure the application can handle high traffic and large datasets effectively.

Sentiment Classification on Social Media Data Using Deep Learning Methods (April 2023 )

Achieved 98% accuracy on 100,000+ COVID-19-related tweets using advanced deep learning algorithms (GCN, GRU, LSTM, CNN, Bi-LSTM) and improved multi-label classification accuracy by 5%.

Designed a zero-shot learning model with S-BERT and knowledge graphs, enhancing flexibility by 20%, reducing processing time by 30%, and improving sentiment predictions by 10% over baseline models.

Implemented an S-BERT-KG model with 92% accuracy for sentiment classification, addressing challenges in single- word class labels.

TECHNICAL PROFICIENCY

Programming Languages: Python, Java, C++, SQL

Machine Learning & AI: Recommender Systems, Collaborative Filtering, Matrix Factorization, Deep Learning (Neural Networks, LSTM, CNN, RBM)

Data Science & Analytics: Pandas, NumPy, Scikit-learn, TensorFlow, Model Evaluation, Performance Metrics, A/B Testing Data Engineering: ETL Pipelines, Data Cleaning, Feature Engineering, Model Optimization Big Data & Cloud: Apache Spark, AWS (EC2, S3, Lambda, SageMaker, DynamoDB), Amazon DSSTNE Visualization: Tableau, Matplotlib, Power BI

DevOps & Version Control: Docker, Kubernetes, Git, GitHub CERTIFICATIONS AND WORKSHOPS

AWS Certified Cloud Practitioner

Basic Image Classification with TensorFlow

Neural Networks and Deep Learning by DeepLearning.AI

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