Siddarth S. — Software Engineer

I like to build stuff. Worked on AI & LLM agents, large-scale backend systems, full-stack & cloud projects, machine learning infrastructure, and data engineering, exploring better ways to do engineering for the job. Some Work Developments Include: ~ Implementing and supporting Blinkit Backend and logistics mobile app with > 10 Million Users ~ Designing and Implementing ML Infrastructure Inference and pipeline for a product with > 50 Million Users. ~ Spearheading core DB scalability through Partitioning and reduced CPU utilization from 60% to 25% via query optimizations. ~ Upgraded AWS Load Balancing and autoscaling for the logistics backend service at Blinkit. ~ Automated a core dashboard by setting up ETL and big data pipelines during my Data Engineer Internship at American Express. ~ Developed an Adaptive Learning Platform and personalized recommendation engine for a product named MicroArc at Guvi. I thrive on challenges and constantly seek opportunities to expand my knowledge in Software Engineering and Data. My Skill Set: ~ Programming Languages: Python, C++, Java, C, JavaScript, TypeScript, SQL, Shell. ~ Domain: Full-Stack Software Development, Data Science, Cloud Computing (AWS, GCP, Azure), Machine Learning, DevOps. ~ Frameworks and Tools: Django, Flask, NodeJS, Docker, Kubernetes, MongoDB, Terraform, Firebase, Neo4j, Hive, PySpark. ~ Libraries: ReactJS, Pytorch, Fastai, Keras, Ethereum, OpenCV, OpenMP, OpenMPI, BeautifulSoup, Selenium, Git. I have also gained valuable research experience in the field of Computer Science. Here are some highlights: ~ AI-Based Diet Management System: Developed a system enabling diet management through food recommendation based on a Deep Residual Neural Network and Graph Database engine. This work resulted in a patent (Patent no: 202041031475) and was hosted on Azure with a Django backend. ~ Application of Neuroevolution in Autonomous Cars: Published an AI research paper exploring the use of genetic algorithms in self-driving cars. The paper was published in Springer CCIS (978-981-16-1244-2). ~ An Alternative C++ based HPC system for Hadoop MapReduce: Conducted research on a C++-based MapReduce framework for high-performance computing, which was published in a De Gruyter journal. Feel free to reach out to me through the following channels: Portfolio Website: https://siddarthsairaj.netlify.app/

Stackforce AI infers this person is a Backend-heavy Fullstack Engineer with expertise in scalable systems and machine learning.

Location: San Jose, California, United States

Experience: 4 yrs 8 mos