About

Sudarshan Hegde is a self-motivated and result-oriented professional with over 15 years of experience in IT. He is a techno-functional pharmacovigilance SME with extended expertise in the domain. Sudarshan has sound knowledge of Oracle Argus Safety and is skilled in technical solutioning and architecting for functional requirements. He has experience in product development, cloud engineering, and product management, change management, product framework designing and developing, and database designing. Sudarshan has expertise in case management and reporting solutions, including AI and ML platforms. He is skilled in software development life cycle (SDLC), database designing, .NET framework, Argus Safety, and C# using microservice architecture for SAAS products. Sudarshan has strong knowledge of E2B R2 and E2B R3 and is capable of defining product roadmap, feature prioritization, feature analysis, and cost estimation. Sudarshan has extended expertise in Argus v7.0.5 till v8.2.1.2 (Oracle HSGBU Product Engineering) and has extensively worked in E2B R2/R3, Argus Japan Case processing, Japan Periodic and expedited reports. He has domain expertise in life science-pharmacovigilance (drug safety) and healthcare. Sudarshan is a persuasive communicator with exceptional relationship management skills and the ability to relate to people at any level of business and management. He is adroit at analyzing organizational requirements and is highly ethical, trustworthy, and discreet. Currently, Sudarshan is working with Insife as a Digital Innovation specialist. He is involved in PV domain uplifted by AI and ML implantation. Sudarshan has been a part of the integral design of the solution to extract data from structured documents/emails/literature to create an adverse event case automatically and assign it to a user for further processing. He has designed a robust, scalable procedure and mainly a cloud-native product that supports SaaS offerings using AWS offerings along with dockization. Sudarshan has also provided solutioning to MHRA for the Covid project, where an E2B R2 XML is fed to PVAI, and PVAI, using AI capabilities, extracts data from various narrative/free text tags, identifies products, events, reaction outcomes, indication, and encoding of MedDra. Then, it sends an extended E2B R2 to the MHRA endpoint. In the past, Sudar