Divpreet Dhingra

Software Engineer

United States0 mo experience

Key Highlights

Developed an Instagram feature for 140M+ users.
Created a chrome extension for job referrals.
Boosted ML model accuracy by 54% in cybersecurity.

Stackforce AI infers this person is a Software Engineer with expertise in AI/ML and frontend development for social media and cybersecurity.

Contact

Skills

Core Skills

Software DevelopmentFrontend DevelopmentApi IntegrationMachine LearningData Analysis

Other Skills

KotlinJetpack ComposeReact.jsTypeScriptTailwind CSSGitsoftware development lifecycle (SDLC)unit testingPythonScikit-learnNumPyPandasTensorFlowFigmaPostgreSQL

About

Hey there! I'm Div, a CMPE Senior @ SJSU and I'm actively recruiting for New Grad SWE opportunities! Don't hesitate to reach out 👨‍💻: divpreet.dhingra@sjsu.edu

Experience

0 mo

Total Experience

Average Tenure

Current Experience

Bank of america

Global Technology Early Insights Fellow

Jan 2024 – Jan 2024 · 0 mo

• Mobile & AI Technologies, Agile Methodologies, Grow Your Career

Refer me

Software Engineer Intern

Dec 2023 – Mar 2024 · 3 mos · Seattle, Washington, United States · Remote

Engineered a chrome extension in React.js and TypeScript to simplify the referral process for 100K+ job candidates
Spearheaded the Quick Referral feature, enabling one-click referral requests by querying refer.me’s internal database of referrers across 1K+ companies
Designed internal tooling that streamlined application development, e.g. a reusable React dropdown component
Relevant skills / technologies: React.js, TypeScript, Tailwind CSS, Git, software development lifecycle (SDLC), unit testing

React.jsTypeScriptTailwind CSSGitsoftware development lifecycle (SDLC)unit testing+2

San jose state university

Artificial Intelligence Intern

Aug 2022 – May 2023 · 9 mos · San Jose, California, United States

Trained and Tested a supervised Machine Learning Model to bypass Malware Obfuscation techniques by using Python libraries to classify API-calls across 121 Malware Families in Google Colaboratory
Boosted ML-Model Accuracy by 54% via ML algorithm refactorization and data extraction, collaborating w/ Team of 9
Retrieved Malware API-call data sets of ∼240 - 250 API calls by sandboxing a 31K Malware-file folder
Constructed C++ program to map Malware Files to Families by parsing 8 csv-sheets of 590K file-family combinations
Relevant skills / technologies: Python, Scikit-learn, NumPy, Pandas, TensorFlow, Virtual Box, collaboration, software development

PythonScikit-learnNumPyPandasTensorFlowMachine Learning+1