Krishna Agarwal

Backend Engineer

Bengaluru, Karnataka, India4 yrs 6 mos experience

Highly Stable

Key Highlights

5 years of experience in backend engineering.
Expertise in building scalable and reliable services.
Proven track record in optimizing performance and reducing costs.

Stackforce AI infers this person is a Backend Engineer specializing in scalable SaaS solutions.

Contact

krishnaagarwal199038@gmail.com LinkedIn

Skills

Core Skills

Backend EngineeringMachine LearningData Engineering

Other Skills

Go (Programming Language)Amazon Web Services (AWS)Software DesignComputer ScienceTeamworkNetworkingAmazon VPCData AnalysisData StructuresAlgorithmsObject-Oriented Programming (OOP)C++HTMLCascading Style Sheets (CSS)Python (Programming Language)

About

I am a Software Development Engineer with 5 years of experience specializing in distributed backend systems. I have designed and built scalable, reliable, and high-performance services, with a strong focus on system design, performance optimization, and production reliability.

Experience

4 yrs 6 mos

Total Experience

3 yrs 6 mos

Average Tenure

1 yr

Current Experience

Uber

SDE 2

Jun 2025 – Present · 1 yr · Bengaluru, Karnataka, India · Hybrid

Backend Engineer

Go (Programming Language)Backend Engineering

Amazon

2 roles

Software development engineer 2

Apr 2024 – Jun 2025 · 1 yr 2 mos

SDE 1

Oct 2021 – Mar 2024 · 2 yrs 5 mos

Building Real-time Machine learning solutions at scale!
1. Worked on building components for our service designated to automate peak events scaling to reduce scattered communication across service owners
a. Evaluated Service latency and optimised Database Indexing impacting latency across 15+ APIs reducing page load time by 15%
b. Built React components for UI and Backend APIs using AWS toolings such as DDB, Lambda and SQS
2. Trained ML models and optimised rulesets for reducing Abuse at Amazon by taking automatic enforcements and avoiding OpEx load to Manual Investigators which helped reduce 76% of Incoming volume for Manual Investigation resulting in $3.5MM annual savings
3. As part of our Model training pipeline, added pipeline steps to improve tracking of business metrics and built adapters to allow scientists plug and play any metric and evaluate model performance by simulating the model decisions reducing the experimentation turn around time from few months to 1 day. Automated ruleset simulation allowing offline simulation reflect same results as Production!

Amazon Web Services (AWS)Software DesignBackend Engineering

Wealth42

SDE

Jun 2020 – Jul 2020 · 1 mo · Bengaluru, Karnataka, India

Extracting information from the web by observing the design patterns across 42 financial websites and writing Python scripts using BeautifulSoup and selenium, data cleaning and building data pipelines.

Software DesignTeamworkData Engineering