Hemant Singh Sengar Research Profile

About

Hemant Singh Sengar is a seasoned Product Manager at Cisco, bringing over 20 years of experience in leading data-driven products from inception to scale. He specializes in data integration, cloud platforms (AWS, GCP), and AI/ML model implementation, with a strong focus on SaaS subscription models. Hemant excels in aligning technology solutions with corporate strategies, driving both cost efficiency and business growth. In his current role, he is responsible for managing product roadmaps, engaging stakeholders, and developing user stories, while ensuring successful user acceptance testing (UAT) and business acceptance testing (BAT). His core competencies include data management, cloud technologies, and AI-powered analytics, including dashboard and chatbot development. Hemant’s previous experience includes positions at Symantec and Zensar Technologies, where he honed his skills in solution architecture and business systems analysis. He has a Bachelor’s degree in Electronics Engineering from S.V.I.T.S Indore and has been instrumental in implementing innovative solutions that enhance business processes, including financial and CPQ processes. With a proven track record of collaboration and leadership, Hemant is committed to delivering high-impact solutions that meet evolving market demands. He is currently open to new product management opportunities that leverage his extensive expertise. Hemant Singh Sengar is a highly experienced Product Manager with over 20 years of expertise in data-driven product management, AI/ML model implementation, and SaaS subscription models. He has a strong background in data integration, cloud platforms like AWS and GCP, and financial processes including ATR, ARR, IACV, and renewals. Hemant is known for his ability to align technology solutions with corporate strategies to drive cost efficiency and business growth. His expertise spans across data management, including data integration, threading, migration, mining, modeling, and visualization, as well as business analytics, AI-powered dashboards, and chatbot development using Gen-AI and ThoughtSpot. He has successfully led product roadmaps, secured stakeholder buy-in, and developed user stories that drive adoption and efficiency. Hemant has extensive knowledge in enterprise software, SaaS products, CPQ processes, and subscription billing, with hands-on experience in Oracle BRM, Snowflake, SAP HANA, and Google Big Query. His proficiency in financial metrics and subscription models has enabled him to implement iACV modernization with Google Cloud Platform and develop AI-powered dashboards that enhance financial forecasting. Currently serving as a Product Manager at Cisco Systems, he has played a key role in transforming recurring revenue metrics, streamlining renewal policies, and integrating financial data models to optimize business insights. Hemant has also been instrumental in implementing ATR and ARR metrics for Cisco’s SaaS and acquisitions, improving cloud-based cross-flow systems, and integrating data across heterogeneous databases such as Oracle and MongoDB. His contributions to AI-driven business intelligence solutions have enhanced sales pipeline visibility and improved financial reporting accuracy. Prior to Cisco, he held key positions at Symantec, where he played a pivotal role in business process transformation, data analytics, and application integration. Hemant’s early career as a software engineer laid a strong foundation for his expertise in Core Java, J2EE, REST web services, Python, R, and Salesforce CPQ. His extensive knowledge of ERP, order management, financials, SaaS billing, and customer account management further solidifies his position as a seasoned professional in the technology and finance sectors. Hemant holds a Bachelor of Engineering degree in Electronics Engineering from R.G.P.V University, India, and has undergone various professional trainings in business analytics, ERP R12, Agile Scrum, Python, SAP HANA, AWS, GCP, and ThoughtSpot. His technical proficiency extends across multiple tools and methodologies, including Tableau, MS Visio, Jira, Agile, and Waterfall, making him a versatile leader capable of driving digital transformation and optimizing product strategies. With a strong ability to analyze business needs, develop scalable data solutions, and implement AI-driven analytics, Hemant continues to be a valuable asset in the field of product management, business intelligence, and SaaS-based financial solutions.

Skills

Experience

Product Manager

Docusign, USA

Nov-2024 to Present

Education

Shri Vaishanv Institute of Technology and Science (SVITS)

B.E in Electronics Engineering

Passout Year: 2001

Publication

Research Article

Punit Goel

December, 2023

Designing Distributed Systems for On-Demand Scoring and Prediction Services

Journal : IJCSP - INTERNATIONAL JOURNAL OF CURRENT SCIENCE

The research focuses on the key design principles required for implementing a distributed system that efficiently handles high-throughput prediction requests while ensuring minimal latency a...

Peer-Reviewed Articles

COMPARATIVE ANALYSIS OF REVERSE IMAGE SEARCH ENGINES USING DIVERSE IMAGE SETS

Eight well-known reverse image search engines—Google, Bing, TinEye, Yandex, Baidu, Getty Images, Shutterstock, and Alamy—are compared in this study based on several different factors. Language support, speed, accuracy, facial recognition, geographic coverage, cropping feature, number of images retrieved, ease of use, mobile app availability, privacy measures, input options, supported file formats, search methods, and additional features are some of these requirements. The study outlines each engine's advantages and disadvantages. Both Google and Bing are very user-friendly, fast, and support multiple languages. However, Google is more accurate and has features like facial recognition and SafeSearch. Yandex offers comparable functionality but targets the Russian market. TinEye promotes privacy and collects very little data, however, it has trouble with unique photos and doesn't have many sophisticated capabilities. Baidu offers little privacy and openness and caters mostly to the Chinese market. Although Shutterstock and Getty Images have extensive privacy policies, their accuracy is not as high. Alamy has a reduced precision of retrieval but complies with data standards. According to the analysis, each engine serves a particular purpose. Google or Bing may be preferred by users looking for smart image detection and user-friendliness. TinEye might work for users who are concerned about their privacy. In the end, the decision is based on personal preferences and search objectives.

DESIGN AND IMPLEMENTATION OF Wi-Fi DEAUTHENTICATION SYSTEM USING NODEMCU ESP8266

Network security is seriously threatened by Wi-Fi de-authentication attacks, which frequently lead to data interception, illegal access, and service interruption. The mechanics and ramifications of these assaults are explored in detail in this research study, which highlights how they could jeopardize network availability, secrecy, and integrity. To bridge theoretical understanding with actual experimentation, the paper presents a practical implementation of a Wi-Fi deauther utilizing the NodeMCU ESP8266 microcontroller platform. With the use of programs like the Arduino IDE and NodeMCU Flasher, the Wi-Fi deauther was created and put through testing to identify and stop de-authentication threats instantly. The system's high detection accuracy, quick response times, and little effect on network performance as a whole are demonstrated by the experimental findings. The NodeMCU ESP8266 platform demonstrated good resource management by managing the detection and countermeasures while keeping CPU use below 70% and guaranteeing less than 5% reduction in network performance and latency. This study advances wireless network security by demonstrating a scalable, affordable method of thwarting de-authentication attacks and by suggesting further improvements that would include machine learning integration and wider assault coverage. For network managers, cybersecurity experts, and researchers looking to strengthen wireless network defenses, the findings offer insightful information and useful recommendations.

Blockchain using Virtual TRY-ON

In today’s dynamic retail environment, the shift towards online shopping necessitates innovative solutions that enhance customer engagement and satisfaction. This project introduces a virtual try-on clothing platform designed to revolutionize the online shopping experience by merging cutting-edge augmented reality (AR) and machine learning technologies. The platform enables users to visualize how garments will fit and appear on their unique body shapes without the need to visit a physical store. By offering a user-friendly interface, the website allows customers to upload personal images or utilize real-time video features, facilitating an interactive and personalized shopping experience. Key functionalities include accurate size recommendations tailored to individual measurements, as well as curated fashion suggestions that align with users' personal styles. These enhancements aim to minimize return rates—a significant challenge in e-commerce—while simultaneously boosting customer satisfaction and driving sales. Additionally, the platform fosters social interaction through built-in sharing capabilities, allowing users to solicit feedback from friends and family, thus enriching the decision-making process. This aspect not only enhances the shopping experience but also builds a sense of community around fashion choices. By integrating advanced technology with a seamless and engaging user experience, this virtual try-on website represents a substantial advancement in online fashion retail. It sets the stage for a more personalized and interactive shopping journey, ultimately redefining how consumers engage with fashion in the digital age. As we look to the future, this platform aims to become a cornerstone of online retail, reflecting the evolving needs and preferences of today’s consumers.

The Power of AI and Machine Learning in Cybersecurity: Innovations and Challenges

Networks and sensitive data are no longer adequately protected by traditional security methods due to the ongoing evolution and sophistication of cyber-attacks. Cybersecurity can be enhanced with the exploitation of machine learning and artificial intelligence techniques, which make threat detection more effective and efficient. This article, while giving an outline of the field's present position, discusses the difficulties in adapting machine learning and artificial intelligence (ML) to cybersecurity. The research discusses machine learning methods that are applied to tasks like malware classification, anomaly detection, and network intrusion detection. Lastly, the necessity for sizable labeled datasets, the adversarial attacks on machine learning models, and the adversity of deciphering models of black-box ML are some of the boundaries and challenges that are also covered.

Design of 4-bit ALU for low-power and High-speed Applications.

This paper presents a novel design and optimization of a 4-bit Arithmetic Logic Unit (ALU) utilizing 90nm CMOS technology, specifically addressing the longstanding carry-out issue prevalent in existing architectures. Notably, our proposed 4-bit ALU architecture successfully minimizes delay and power consumption by incorporating an optimized carry-out design employing AND gates. A comprehensive comparison of three logic styles - Pass Transistor Logic (PTL), Complementary Metal-Oxide-Semiconductor (CMOS), and Transmission Gate Logic (TGL) - is conducted, yielding significant improvements in power-delay tradeoffs. Simulation results validate the efficacy of our design in resolving the carry-out issue, making it an attractive solution for low-power, high-speed digital applications.