Vinayak Siddhu B.

Software Engineer | Machine Learning Engineer | Cloud & Distributed Systems Specialist
Arlington, US.

About

Highly skilled Software Engineer with a Master's in Computer Science and expertise in building scalable, real-time distributed systems, advanced machine learning solutions, and robust data pipelines. Proven ability to optimize performance, enhance system reliability, and drive significant improvements in classification accuracy and operational efficiency, leveraging AWS, Spring Boot, and Python. Seeking to apply deep technical knowledge and problem-solving capabilities to develop innovative software solutions in a dynamic environment.

Work

Walmart USA
|

Software Engineering Intern

Remote, US

Summary

Developed and optimized high-performance distributed systems, designing scalable data pipelines and collaborating with cross-functional teams to deliver production-ready solutions.

Highlights

Developed and optimized a Distributed Heap Data Structure in Java for high-performance scheduling, enabling the simulation of large-scale real-time workflows.

Designed comprehensive UML diagrams and ERDs to support robust real-time data pipelines and scalable databases, enhancing system architecture.

Collaborated effectively with cross-functional teams, leveraging Agile methodologies, CI/CD practices, and production-ready deployments to ensure seamless project delivery.

Accenture North America
|

Development & Engineering Simulation

Remote, US

Summary

Engineered distributed microservices and integrated CI/CD pipelines to simulate production-grade systems, collaborating with QA, Product, and DevOps to meet stringent performance goals.

Highlights

Built distributed microservices using Java and Spring Boot, simulating production-grade systems to ensure robust and scalable solutions.

Integrated Jenkins CI/CD pipelines for automated builds and deployments, significantly improving iteration speed and development efficiency.

Partnered with QA, Product, and DevOps teams to ensure sprint deliverables consistently met stringent system performance goals and quality standards.

Advantage Ecosystem
|

Backend Engineer

Hyderabad, India, India

Summary

Designed and implemented scalable backend infrastructure, optimizing API integrations and database performance for high-traffic web applications at Advantage Ecosystem.

Highlights

Built a scalable backend infrastructure supporting high-traffic web applications, ensuring robust performance and reliability for critical services.

Implemented critical API integrations and database optimizations, enhancing real-time performance for key system functionalities.

Engaged in community events focused on Cloud Engineering and Machine Learning, sharing best practices and contributing to knowledge dissemination within the tech community.

Education

University of Texas at Arlington
Arlington, TX, United States of America

Master of Science

Computer Science

Institute of Aeronautical Engineering
Hyderabad, India, India

Bachelor of Technology

Computer Science and Information Technology

Languages

English

Skills

Programming Languages

Java, Python, C++, Node.js, Shell, SQL, Lua, Go.

Databases

NoSQL, DynamoDB, MySQL.

Frameworks

Spring Boot, React, Django, Node.js.

Cloud & DevOps

AWS, Docker, Kubernetes, Jenkins, Git, CI/CD, Distributed Systems.

Specialized Areas

Real-Time Systems, Data Pipelines, LLMs, ML Frameworks, Microservices, REST APIs.

Tools

Linux, Kafka, Grafana, CloudWatch, TensorFlow, PyTorch, Jira.

Projects

Prompt Engineering Platform & Governance System

Summary

Designed and developed a secure, governed Prompt Engineering Platform to enable engineers to build robust LLM-based systems, incorporating automated testing and adversarial analysis with Django, Python, CI/CD, and LLM Testing.

Enterprise-Scale Emotion Recognition System

Summary

Developed a comprehensive, real-time emotion recognition system leveraging distributed microservices, cloud-native data pipelines, and fine-tuned machine learning models using Spring Boot, AWS, PySpark, and Grafana.

Music Emotion Recognition Using Deep Neural Networks

Summary

Researched and developed a deep neural network-based system for music emotion recognition, achieving high accuracy with real-time audio features using Python, TensorFlow, and PyTorch.