Aayush Ranjan

Software Developer

IN.

About

Highly skilled Software Developer with a proven track record of developing innovative solutions and optimizing system performance. Recognized for leading a 7-member team to win JP Morgan Chase's Code For Good '24 Hackathon, demonstrating exceptional problem-solving and leadership abilities. Possessing expertise in AI/ML model development, secure application architecture, and full-stack development, eager to apply advanced technical skills to drive impactful projects in a dynamic technology environment.

Work

Samsung Research and Development Institute India

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Research and Development Intern - PRISM

Remote, Global, XX

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Summary

As a Research and Development Intern in PRISM, Aayush is currently developing an AI-augmented security pipeline with CI/CD integration to automate vulnerability detection and remediation.

Highlights

Leading the development of an AI-augmented SAST/DAST security pipeline, integrating CI/CD via GitHub Actions and leveraging tools like Bandit, Semgrep, and OWASP ZAP to enhance application security.

Automating vulnerability detection, classification, and remediation suggestions using advanced NLP and LLM techniques, streamlining security processes.

Carelon Global Solutions India

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Technology Intern

Bengaluru, Karnataka, India

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Summary

As a Technology Intern, Aayush enhanced system visibility and authentication performance by building RESTful APIs and integrating real-time data streaming solutions for a project tracking platform.

Highlights

Increased visibility into project deliverables by 33% through the development and documentation of 10+ RESTful APIs using Spring Boot, JPA, MySQL/H2, and Swagger.

Accelerated authentication performance by 50% by seamlessly integrating Spring Security with Okta for robust role-based access control.

Implemented Apache Kafka for real-time data streaming, enabling asynchronous communication across microservices and improving system responsiveness.

Actively contributed to Agile sprints, code reviews, and feature development using JIRA and Bitbucket, ensuring timely project delivery.

Education

Vellore Institute of Technology

Vellore, Tamil Nadu, India
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Bachelor of Technology (BTech) Computer Science Grade: CGPA: 9.1

Delhi Public School

Ranchi, Jharkhand, India
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Central Board of Secondary Education PCM + CS Grade: PC: 93%

Awards

Winner - JP Morgan Chase's Code For Good '24 Hackathon

JP Morgan Chase

Recognized as a top 0.3% participant (200 out of 53,000+ applicants) across India, leading a 7-member team to develop the winning non-profit solution for rural accessibility.

Finalist - Women Techies '24 Hackathon

Women Techies

Achieved top 5 among 200+ teams for developing Yogasana, a browser-based ML app utilizing PoseNet for real-time yoga pose correction.

Certificates

Azure AI Engineer Associate: AI102

Microsoft

Azure Data Scientist Associate: DP100

Microsoft

Azure AI Fundamentals: A1900

Microsoft

Azure Data Fundamentals: DP900

Microsoft

Generative AI with Large Language Models

DeepLearning.AI and AWS

Learning Linux Shell Scripting

LinkedIn Learning

Skills

Languages

Python, Java, Go, C/C++, SQL, JavaScript, HTML, CSS.

Frameworks

Spring Boot, React, Node.js, Express.js.

Developer Tools

Apache Kafka, Linux, Git, Docker, Kubernetes, Bitbucket, JIRA, Jupyter Notebooks, Firebase.

Libraries

TensorFlow, PyTorch, scikit-learn, NumPy, Pandas, LangChain.

Core Concepts

Data Structures & Algorithms, Operating Systems, Database Systems, Computer Networking.

Soft Skills

Communication, Teamwork, Leadership, Problem Solving, Adaptability, Time Management.

Projects

GoChat

Summary

Developed a real-time chat application featuring sub-100ms message delivery latency and supporting up to 100 concurrent users via WebSocket protocol.

SkimLit

Summary

Created an NLP model to classify abstract sentences into roles, achieving 81% training accuracy on the PubMed 20k RCT dataset.

Yogasana (Women Techies '24 Hackathon)

Summary

Built a browser-based ML app utilizing PoseNet for real-time yoga pose correction, enabling in-browser pose recognition with under 100ms latency.