Jayminbhai Kanubhai Vankar

Initiated and built data automation AI tool for business processes to decrease pre-modeling data processing time by 70%, and increase efficiency of data-driven decision making by 50%. Designed and executed comprehensive strategy to automate structured data processing ensuring fast and flexible data handling and transformation, starting with user stories, backend and frontend web architecture, database connection, APIs, logic building, rule engine, OCR model, testing framework, and cloud integration. Implemented data-outliers-related alert system for manufacturing plant that resulted in improved response time. Created Generative AI tool to develop web platform with minimal developers’ input catering to pre-sales client demo, resulting into 60% reduction in query response time, and significant improvement in client engagement. Integrated GPT3.5 model into the local system and generated codebase using prompt engineering techniques to automate SDLC as per requirements, deployed web platforms into Azure after peer code review. Developed a program to facilitate micro-frontend and micro-services enabling the effective functioning of various services, resulting in better client satisfaction score and user experience. Modified codebase and segregated different functionality of web application, deployed it on Azure using docker containers, Azure Kubernetes, Blob Storage, Azure DevOps, and other services. Established operating procedures to use Cognite Data Fusion (CDF) for handling client’s data, leading to improved data quality control, workflow efficiency, seamless dataops, and faster data processing. Used CDF’s Open Industrial Data (OID) to present client demos, created data transformation, contextualization, statistical analysis, and PowerBI dashboards using CDF AI-based DataOps Platform and Training Modules. Digitalized working of a health care client. Built features in a web application like records of medical history, prescriptions, scheduling of appointments, and feed

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