Anwesh Peddineni

Software Engineer

Sunnyvale, California, United States6 yrs 9 mos experience

Key Highlights

Proven expertise in building scalable backend systems.
Strong foundation in algorithms and cloud computing.
Experience in developing impactful microservices.

Stackforce AI infers this person is a Backend-focused Software Engineer with expertise in Fintech and E-commerce solutions.

Contact

Skills

Core Skills

Identity And Access Management (iam)JavaMachine LearningPythonMicroservices

Other Skills

Role-Based Access Control (RBAC)React.jsDjangoMySQLJenkinsAmazon EKSAmazon Web Services (AWS)Elastic Load BalancingJUnitRedisNode.jsHibernateSpring BootREST APIsGit

About

An effective Software Engineer focused on building reliable, scalable backend systems with clear ownership and measurable impact. I spend most of my time coding, debugging, testing, documenting, and collaborating, while contributing to design when it adds value. I strongly believe software engineering is a team effort, and I value teamwork, openness, and team spirit—enabling everyone to do great work together. I’m motivated by environments where ideas are heard, feedback is honest, team success is prioritized, and it’s safe to take risks and learn quickly. I continuously improve my mental model to adapt and learn fast, driven by curiosity and motivation to deliver impact in challenging roles. I apply computational thinking to break down complex problems into simple, scalable, and reliable solutions. I care deeply about building maintainable, readable systems that evolve well over time, creating a great teammate experience, and optimizing for outcomes—customer experience, system correctness, performance, and revenue. I have strong fundamentals in Algorithms, Backend Development, Cloud Computing, Distributed Systems, and Applied ML.

Experience

6 yrs 9 mos

Total Experience

2 yrs 1 mo

Average Tenure

4 mos

Current Experience

Google

Software Engineer

Feb 2026 – Present · 4 mos · Sunnyvale, CA · Hybrid

Intuit

Software Engineer

Jun 2025 – Jan 2026 · 7 mos · Mountain View, CA · Hybrid

Cloud native computing foundation (cncf)

Open Source Developer

Mar 2025 – May 2025 · 2 mos · San Francisco, California, United States · Remote

Role-Based Access Control (RBAC)Identity and Access Management (IAM)Java

Algobrainz llc

Software Engineer

Aug 2024 – Jan 2026 · 1 yr 5 mos · Remote

Northeastern university

Research Assistant

Sep 2023 – Jun 2024 · 9 mos · Boston, Massachusetts, United States

Developed an equities research tool for a finance application, leveraging AI models and ML algorithms to provide 80% accurate equity recommendations based on SEC 13F filings data. Integrated REST APIs to save users 30% of their time
Generated the required dataset using web scraping with Beautiful Soup, followed by comprehensive EDA, data cleaning, and feature engineering using Pandas and Numpy
Employed various scikit-learn classifiers, including Random Forest as a binary classifier, initially achieving 77% accuracy in distinguishing between good and bad investor transactions across the train, cross validation, test datasets
Expert profile is created by understanding area of expertise in the public equity markets from the historic investment data of each expert by making use of feature engineering
Building the UI with React JS and Backend Services with Python, Django and MySQL

React.jsDjangoMachine LearningPythonMySQL

Qwipo

Software Engineer

Sep 2020 – Aug 2022 · 1 yr 11 mos · India

Developed REST APIs for an e-commerce microservices app using Spring Boot, Hibernate, and Node.js, covering 30% critical use cases, ensuring high-quality solutions in an Agile SDLC.
Refactored a Java Inventory Microservice using Design Patterns, achieving 90% test coverage, reducing technical debt by 30%, and accelerating new feature development timelines by 25%.
Built a real-time Analytics Microservice with Spring Boot, Kafka, and React.js to aggregate KPIs, cutting data gathering time by 50%.
Optimized scalable Spring Boot APIs for Order Management with async processing, Redis caching, Feign, and distributed tracing, boosting throughput by 25%.
Migrated a I/O intensive Catalog Microservice from Spring Boot to Node.js. Used async/await and Redis caching, reducing response times by 35% and doubling throughput compared to the Spring Boot application.
Deployed services on Amazon EKS, reducing costs by 15%. Automated infrastructure with Terraform/Jenkins CI/CD, boosting throughput by 25% and reliability by 30%.
Enhanced ReactJS UI with lazy loading, debounce search, CSS shimmer effects, and React portals, reducing load times by 30%.

JenkinsAmazon EKSAmazon Web Services (AWS)Elastic Load BalancingJUnitRedis+10