Summary
Featured Merchant Algorithm, Context Aware Precompute
• Export Consistency Service - Workflow for measuring consistency of Featured Offers of Exports use-case in our data stores
• Designed a robust sampling strategy to obtain a list of ASINs to ensure fair representation of ASINs for workflow to run upon.
• Designed and implemented the workflow to compute consistency of our featured offers in exports use-case with a latency
reduced by 50 ms on an average by the use of caching over external components which vend static configuration
• Deployed the workflow as a long running Fargate service on AWS ECS and set up corresponding monitors and alarms
• All Buying Option Support - Project to support multiple buying options in our precompute system, which increased the
accuracy of our precompute system by 1.4%
• Designed an enhanced ASIN sampling strategy to enable testing of changes in order to support various buying options with a
fallback mechanism to ensure representation of ASINs having buying option type with very low view traffic
• Onboarded static configuration required to fetch different buying options to Amazon internal config store and developed the
client required to fetch these configurations.
• Enhanced the logic of accuracy computation in order to incorporate the support of different buying option types,
• Segments Data Publisher - Service to publish data from S3 bucket to Amazon internal data store
• Created an end to end resilient workflow from using AWS Step Functions and AWS ECS to publish segments data from S3 to
our data store
• Implemented multi-threading for optimal usage of the ECS Fargate computing allowing to publish data at a rate of 20000 TPS
• Implemented checkpointing mechanism to ensure smart handling in case of ECS task failure and subsequent retry with Step
Functions
Search Relevance, Non-Default Sort
• Ranking Model Evaluation Framework - Framework to evaluate various ranking models in production used to rank search
results of customer queries
• Engineered the framework from end to end with various AWS technologies like AWS Step Functions, AWS Lambda and AWS
EMR Serverless to calculate various metrics and dump the result in a S3 bucket
• Integrated the framework with a Query Replay Service via collaborating with multiple teams to automate extraction of
customer queries with a high TPS and used the extracted logs for computation of various metrics required, reducing the
manual effort required in evaluation by Tk person-hours
• Configuration for A/B Testing of Ranking Models
• Worked on configuration for A/B testing and launch of various ranking models in production, with a projected annualized
impact of more than $1,000,000,000 worldwide in 2023