Operant
Vrajesh B. has a diverse and extensive work experience. Vrajesh is currently the Co-founder & CEO of Operant, a runtime application protection platform for cloud-native environments. Prior to this, they worked as the Director of Product & Market Development at Scaled Inference, where they led the GTM and product efforts for their reinforcement learning SaaS platform.
Before that, Vrajesh held the position of Senior Manager - ML Technology and Ecosystem Products at Arm, where they incubated a business unit for machine learning products and built a collaborative ecosystem with over 50 partners including Google, Facebook, and Amazon.
Vrajesh also has experience at Qualcomm, where they started as a Senior R&D Engineer and later transitioned to a commercial role. At Qualcomm, they supported research projects, technology evaluations for investments, and was involved in engineering tasks related to cloud elasticity, parallel architecture for web browsers, and JIT compiler development.
Earlier in their career, Vrajesh worked as a CoreOS Engineer at Apple Inc., where they contributed to the development of iOS and OS X kernel features and was involved in hardware reviews, panic debugging, and compiler projects.
Vrajesh began their career at Mindspeed Technologies as a Systems Engineer, where they worked on networking solutions, device drivers, and validation software.
Overall, Vrajesh B. has a strong background in technology, product development, strategic partnerships, and leadership roles across various industries.
Vrajesh B. pursued a Bachelor's degree in Computer Engineering from Gujarat University, which they completed from 1999 to 2003. Subsequently, they enrolled at the University of Southern California from 2003 to 2005, where they obtained a Master's degree in Computer Science.
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Operant
Operant offers automatic discovery and analytics, real-time risk rankings, and intelligent runtime enforcement. They offer real-time catalogs for all of your APIs, services, and RBACs, across every cluster and cloud, as well as quick visibility of API endpoints, service interactions, and security vulnerabilities. With risk-weighted scorecards thatare prioritized by criticality, access, and exposure, they consistently highlight the security gaps with the highest level of severity. Contextual controls are used to impose fine-grained security constraints on legacy endpoints, data storage, and APIs.