Ravindra Chittapur

CTO

Bengaluru, Karnataka, India18 yrs 9 mos experience

Most Likely To SwitchHighly Stable

Key Highlights

Led high-impact projects with billions of requests daily.
Expert in designing scalable systems with low latency.
Strong background in data structures and algorithms.

Stackforce AI infers this person is a Backend-heavy Fullstack architect in the AdTech industry.

Contact

ravindra.chittapur@gmail.com LinkedIn

Skills

Core Skills

ScalabilityDistributed Systems

Other Skills

Ad personalizationAd selection optimizationAgile MethodologiesBudget distributionData collection pixelData synchronizationDynamic Distribution AlgorithmHadoopHibernateHigh throughput processingIntegration with SSPsLinuxPerlReal-time bidding engineReal-time data processing

About

• Development experience in Java, C • Experience in designing & building from scratch, large scale systems handling billions of requests per day with single digit millisecond latencies • Experience in designing for scale, maintenance and operational efficiency • Have owned and delivered on high impact projects for the company • Have lead talented engineers on critical projects • Good analytical, problem solving and debugging skills • Data structures and algorithms • Experience building scalable web applications • Design patterns & OOP

Experience

Flipkart

4 roles

Senior Principal Architect

Promoted

Apr 2022 – Present · 3 yrs 11 mos

Shopping Experience

Principal Architect

Promoted

Oct 2019 – Mar 2022 · 2 yrs 5 mos

Ads, Recommendations, Merchandising, CRM and Marketing Platforms

Senior Architect

Jul 2017 – Sep 2019 · 2 yrs 2 mos

Architect

Aug 2015 – Jun 2017 · 1 yr 10 mos

Architect for ads @ Flipkart

Komli media

2 roles

Principal Software Engineer

Apr 2014 – Jul 2015 · 1 yr 3 mos

Lead Software Engineer

Apr 2011 – Mar 2014 · 2 yrs 11 mos

Notable Projects:
Ad personalization (Dynamic Creative Optimization)
Lead the project from conceptualization to design and development. The idea is to capture user’s actions on the advertiser’s website and utilize the information to personalize the ad creative for each user. The user’s actions captured can also be utilized for recommending products to the user via ads. Primary features include:
Smart data collection pixel
Advertiser catalogue parsing
Near real-time data pipeline
Ad selection optimization
Creative personalization
Dynamic (Budget) Distribution Algorithm
Designed and developed a dynamic, robust, self-correcting distribution algorithm to distribute campaign budgets across geographically separated data centres. Due to the real-time nature of our ad-servers and the latency SLA’s that they operate under, it is necessary that campaign budget is available at each data centre locally. The efficiency with which we distribute the budget across data centres determines how much business is done on the platform. The primary characteristics of the algorithm include:
Distribution optimized to increase total spend
Respond to changes in spend trend of different datacentres
Optimal rate of readjusting the distribution
Prevent toggling i.e. have smooth increasing/decreasing curves Vs zigzag curves
Faster recovery at low split ratios
Real-time bidding engine(RTB bidder)
Was instrumental in designing and developing a real time bidding platform to buy ad impressions in real time auctions conducted by SSPs. The platform currently handles billions of requests per day with single digit millisecond latencies. Integrated the bidding platform with major SSPs of the industry like Google AdeX, Rubicon, Pubmatic & FbX.
Real-time user store
The store is designed to:
Handle data of hundreds of millions of users
Scale horizontally
Sync data across datacentres
Support throughput of 100K transactions per second with less than a millisecond latency.