Prerit Gupta profile photo 1 on Tal

Prerit Gupta

Associate Consultant @ OFSS | Python, Java, OracleDB | IIT Mandi Research | Ex-HCLTech, DRDO Intern

India

3+ years of work experience

ORACLE FINANCIAL SERVICES SOFTWARE LIMITED
Aug 20242 yrs 1 moPresent

Pune District, Maharashtra, India

• Refactored legacy modules and deployed scalable features across financial platforms, boosting performance and maintainability through Agile practices, daily Scrum collaboration, and clean Java backend architecture. • Delivered enhancements within Agile sprints using Struts, Java, J2EE, OracleDB, PL/SQL, SpringBoot, JavaScript, and Oracle Cloud. Contributed to AI Innovation Hub and Delivery Innovation Hub: • Led a high-impact GenAI PoC automating business workflows with LLMs and HITL, reducing manual effort by 75% and establishing a reusable AI framework within cross-functional teams. • Built adaptive solutions using OCI Generative AI, Langchain, Python, Oracle Apex, and showcasing innovation, fast learning, and technical versatility in enterprise-scale AI applications.
Indian Institute of Technology, Mandi
Jan 20247 moAug 2024

Remote

Engineered a high-performance lossless image compression system using hierarchical dictionary learning, outperforming WebP by 1.5× and JBIG2 by 3×; demonstrated strong analytical thinking and research rigor. Built pipelines with Python, NumPy, multi-level dictionaries (16×16 to 2×2), and benchmark datasets.
HCLTech
Jul 20231 moAug 2023

Remote

Got an opportunity for 6-Weeks Remote Internship Program under Dr. Balaji Rao. Played a vital role in the development of a Face Anti-Spoofing System, focusing on leveraging the PyWavelets Library to compute Complex Wavelets for Face Images. Engineered a Multiscale Energy computation pipeline to analyze and differentiate between Original and Fake Face Images. Applied a range of Supervised Learning Models and implemented advanced ANN and CNN Models, including VGG-16 and AlexNet. Achieved a notable 87% accuracy in distinguishing between Genuine and Spoofed Face Images by employing the Random Forest Algorithm. The experience gained in this project sets the stage for future innovations in biometric security systems. The acquired insights can be leveraged to refine anti-spoofing algorithms and develop more sophisticated models for enhanced security applications.
Defence Research and Development Organisation (DRDO)
May 20233 mosJul 2023

Delhi, India

Collaborated with a team of 2 members and worked under Scientist Deepak Kumar Verma. The project is focused on analyzing the effectiveness of various machine learning models in detecting network attacks on different protocols, such as TCP and UDP. Rigorously assessed and compared the accuracy of multiple Supervised Learning Algorithms in detecting network attacks on different protocols. Demonstrated the efficacy of the employed methodologies through empirical analysis and evaluation. Achieved a notable accuracy rate of 74% utilizing the Random Forest Algorithm for network attack detection. The successful outcomes of this project lay the foundation for further research and development in the field of network security. The insights gained can be utilized to refine existing detection systems and develop more robust models for safeguarding network infrastructure.
GitHub
@Prerit007

Prerit’s AI Stack

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Education

Shiv Nadar University

Bachelor of Technology - BTech, Computer Science

Shiv Nadar University

20204 yrs2024

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Headline

Associate Consultant @ OFSS | Python, Java, OracleDB | IIT Mandi Research | Ex-HCLTech, DRDO Intern

About

As a results-driven consultant and researcher, I specialize in building AI and data-driven solutions that solve complex business challenges—especially in the financial technology space. At Oracle Financial Services Software, I’ve enhanced enterprise platforms and pioneered GenAI workflows that reduced manual effort by 75% and set new standards for automation. My research at IIT Mandi delivered a novel image compression system that outperformed leading benchmarks, showcasing my passion for innovation and analytical rigor. With hands-on experience across Python, Java, OracleDB, and modern machine learning libraries, I thrive in fast-paced, cross-functional teams and enjoy tackling problems from end to end. Whether building adaptive AI with LangChain and OCI or experimenting with LLMs and agentic tools, I’m committed to continuous learning and impactful delivery. Let’s connect if you’re interested in AI-driven transformation, data engineering, or collaborating on breakthrough technologies!

Location

India

AI stack

codex

cursor

opencode

Work experience

Associate Technical Consultant at ORACLE FINANCIAL SERVICES SOFTWARE LIMITED, Aug 2024 - Present · 2 yrs 1 mo, Pune District, Maharashtra, India, • Refactored legacy modules and deployed scalable features across financial platforms, boosting performance and maintainability through Agile practices, daily Scrum collaboration, and clean Java backend architecture. • Delivered enhancements within Agile sprints using Struts, Java, J2EE, OracleDB, PL/SQL, SpringBoot, JavaScript, and Oracle Cloud. Contributed to AI Innovation Hub and Delivery Innovation Hub: • Led a high-impact GenAI PoC automating business workflows with LLMs and HITL, reducing manual effort by 75% and establishing a reusable AI framework within cross-functional teams. • Built adaptive solutions using OCI Generative AI, Langchain, Python, Oracle Apex, and showcasing innovation, fast learning, and technical versatility in enterprise-scale AI applications.

Research Assistant at Indian Institute of Technology, Mandi, Jan 2024 - Aug 2024 · 7 mo, Remote, Engineered a high-performance lossless image compression system using hierarchical dictionary learning, outperforming WebP by 1.5× and JBIG2 by 3×; demonstrated strong analytical thinking and research rigor. Built pipelines with Python, NumPy, multi-level dictionaries (16×16 to 2×2), and benchmark datasets.

Software Engineer Intern at HCLTech, Jul 2023 - Aug 2023 · 1 mo, Remote, Got an opportunity for 6-Weeks Remote Internship Program under Dr. Balaji Rao. Played a vital role in the development of a Face Anti-Spoofing System, focusing on leveraging the PyWavelets Library to compute Complex Wavelets for Face Images. Engineered a Multiscale Energy computation pipeline to analyze and differentiate between Original and Fake Face Images. Applied a range of Supervised Learning Models and implemented advanced ANN and CNN Models, including VGG-16 and AlexNet. Achieved a notable 87% accuracy in distinguishing between Genuine and Spoofed Face Images by employing the Random Forest Algorithm. The experience gained in this project sets the stage for future innovations in biometric security systems. The acquired insights can be leveraged to refine anti-spoofing algorithms and develop more sophisticated models for enhanced security applications.

Research Intern at Defence Research and Development Organisation (DRDO), May 2023 - Jul 2023 · 3 mos, Delhi, India, Collaborated with a team of 2 members and worked under Scientist Deepak Kumar Verma. The project is focused on analyzing the effectiveness of various machine learning models in detecting network attacks on different protocols, such as TCP and UDP. Rigorously assessed and compared the accuracy of multiple Supervised Learning Algorithms in detecting network attacks on different protocols. Demonstrated the efficacy of the employed methodologies through empirical analysis and evaluation. Achieved a notable accuracy rate of 74% utilizing the Random Forest Algorithm for network attack detection. The successful outcomes of this project lay the foundation for further research and development in the field of network security. The insights gained can be utilized to refine existing detection systems and develop more robust models for safeguarding network infrastructure.

Education

Bachelor of Technology - BTech, Computer Science, Shiv Nadar University

Public links

github: https://github.com/Prerit007