Chirag Sachdeva

Software Engineer

San Francisco, California, United States6 yrs 10 mos experience

Most Likely To SwitchAI ML Practitioner

Key Highlights

Developed Twitch's first real-time monitoring system.
Led migration of monetization services to Amazon infrastructure.
Reduced subscription latency by 30% and operational costs by $10M.

Stackforce AI infers this person is a SaaS expert with a strong focus on cloud infrastructure and distributed systems.

Contact

chigsachdeva@gmail.com LinkedIn

Skills

Core Skills

Cloud ComputingDistributed SystemsSoftware Development

Other Skills

AJAXAPI DevelopmentAPI developmentAWSAgile Software DevelopmentAlgorithmsAnalytical solutionsAngularJSArtificial Intelligence (AI)AutomationBackend developmentBeautifulSoupCC++Cascading Style Sheets (CSS)

About

Chirag Sachdeva is a software engineer specializing in scalable systems, real-time monitoring, cloud infrastructure, and distributed computing. At Twitch (Amazon), he built Twitch’s first client-side monitoring system, now a company-wide standard, improving uptime, real-time outage detection, and preventing millions in revenue loss. He also led the migration of Twitch’s monetization services to Amazon, optimizing performance, cutting latency by 30%, and reducing infrastructure costs. With a Master’s in Computer Science from CMU and research experience at Stanford and Georgia Tech, Chirag has expertise in backend (Go, Java), frontend (React, TypeScript, Next.js), cloud infrastructure (AWS, Docker, Kubernetes), real-time analytics, and security. Passionate about high-performance, resilient architectures, he excels in building mission-critical systems that drive business success.

Experience

Twitch

2 roles

Software Engineer II

Promoted

Jan 2023 – Present · 3 yrs 2 mos · San Francisco, California, United States

At Twitch, I lead engineering initiatives that improve monetization, user experience, and system reliability, directly impacting millions of users and streamers while driving millions in revenue and cost savings.
Some of my previous accomplishments include:
o Designed and deployed Twitch’s first real-time client-side monitoring system for the Commerce org, enabling proactive detection of user experience issues across 10M+ DAUs. This system, later adopted platform-wide, reduced incident detection time from hours to minutes, increased feature availability to 99.99%, and prevented $30M+ in annual revenue loss.
o Architected and implemented key monetization features end-to-end, from infrastructure to UI—including Turbo, Gift Discounts, Creator Dashboard, Quick Actions, Founders Badge, and Custom Subscription Benefits—driving millions in revenue and user engagement.
o Led a full-scale migration of Twitch’s monetization services to Amazon infrastructure, rewriting core services and optimizing backend systems. This reduced subscription & gifting latency by 30%, eliminated 4 legacy services, cut annual operational costs by $10M, and improved system maintainability.
o Championed cross-functional initiatives, collaborating with product, infra, and data teams to align engineering roadmaps with business growth, ensuring seamless feature rollouts and launch monitoring.
With a strong foundation in backend, frontend, and distributed systems, I specialize in scaling high-traffic systems, improving real-time monitoring, and optimizing monetization platforms.

Real-time monitoringCloud infrastructureDistributed systemsBackend developmentFrontend developmentCloud Computing+1

Software Engineer I

Jun 2021 – Dec 2022 · 1 yr 6 mos · San Francisco, California, United States

Carnegie mellon university

Graduate Teaching Assistant

Jan 2021 – May 2021 · 4 mos · Pittsburgh, Pennsylvania, United States

o Collaborated with 7 TA’s to implement a regression test suit to automate assignment gradings for Web Application course. Added automatic git cloning and source code execution by spinning up a docker container. Delivered intelligent error messages using NLTK, penalty points and incorporated code injection protection. Slashed grading times by 90%. Used BeautifulSoup, Docker, Selenium WebDriver.
o Conducted weekly office hours to help 150+ students with the course assignments and projects.

AutomationDockerSelenium WebDriverBeautifulSoupSoftware Development

Institute for software research

Software Developer

May 2020 – Dec 2020 · 7 mos · Pittsburgh, Pennsylvania, United States

o Developed a distributed web crawler for a Search Engine to analyze security protocol and SSL certificate information of websites; responsible for all phases (design, version control, coding, unit testing and production).
o Crawled through 4.3 million URLs per day using multithreaded parallel breath search of domains/subdomains based on priority values.
o Decreased latency time and improved data exchange rate between sockets by 4x utilizing encrypted serialized data objects in Java.
o Built cloud infrastructure using AWS Elastic Beanstalk for storing, querying large data, and displaying results on a JSP web application.

Distributed systemsJavaAWSWeb developmentDistributed Systems

Stanford university

Researcher

May 2017 – Aug 2017 · 3 mos · Palo Alto, California

o Designed and implement fly-by-feel, a framework which leverages high-dimensionality and multimodality properties of sensor data to predict response of bio-inspired smart wing for autonomous vehicles under different flight conditions and loads.
o Decreased memory usage by 40% by developing APIs for dynamic allotment of shared memory access key pertaining to flight states.

API developmentMemory management

Georgia institute of technology

Visiting Researcher

May 2016 – Jul 2016 · 2 mos · Atlanta, Georgia

o Developed intrinsic equations and coded a tool using symbolic programming in Mathematica to predict the behavior of pre-twisted and initially curved smart beams 100 times faster than commercially available analysis tools (ABAQUS, ANSYS).

Symbolic programmingMathematica

Indian institute of technology, ropar

Research Fellow

May 2015 – May 2016 · 1 yr · Ropar, India

o Formulated non-linear asymptotically accurate analytical solutions to predict mechanical response of functionally graded structures subjected to variable loads/boundary conditions and reduced computational time by 90%.
o Developed a tool using Python for flexible modeling and effective analysis of composite microstructures based upon an input user file containing the desired parameters to reduce pre-processing time.

Analytical solutionsPython