Hitesh G Email & Phone Number

Bangalore Urban, Karnataka, India

India

Served as Team Lead, Scrum Master, Software Architect and a Subject Matter Expert across multiple high-impact projects at Betsol, demonstrating versatile leadership and technical acumen.Orchestrated and executed comprehensive project plans within a Scaled Agile Framework, meticulously aligning each program increment with overarching project goals and.

Bengaluru, Karnataka, India

Successfully migrated approximately one million Salesforce records to HubSpot CRM, achieving zero application downtime and ensuring seamless data transitionArchitected and developed key integrations for ZRM, a legacy product, into Zmanda, thereby enhancing database backup and recovery capabilities for MySQL and MariaDB databasesEngineered integrations with.

India

Spearheaded research and development to bolster zmanda's license generation using advanced cryptographic algorithmsSignificantly fortified the security of the licensing module by introducing multi layered security measures, including key management and authorization protocolEnhanced license file decryption speed by 40% through optimized parsing.

India

Application of descriptive statistics, Data visualization, and developing prediction model (using specific Machine learning algorithms) for the respective use cases using RDesigned a model based on regression for the respective use case of sales advt Resume content 4Designed a model based on regression for forecasting the prices of bike rentals for a.

India

Analyzed large datasets from multiple sources to identify patterns and trends in the data.Prepared reports that summarized analysis findings and presented them to senior management.Collaborated with cross-functional teams across various departments.Implemented supervised learning techniques such as logistic regression, decision trees, and support vector.

India

Developed a Python-based audio genre detection application using machine learning.Created a machine learning model utilizing spectrogram analysis for genre classification.Utilized deep learning techniques and CNN architecture for accurate genre predictions.Curated and preprocessed diverse audio datasets for model training and validation.Achieved high.