Conversational AI @Google Cloud

https://cloud.google.com/solutions/customer-engagement-ai

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I currently work in 'AI Labs' at Expedia. I was fortunate to help build a common AI platform from ground up.

Specifically my team is involved in powering the conversations platform for Expedia and all its brands.

My Current work involves building a highly available, fault tolerant AI Platform and ML pipelines. Specifically i worked on

• Deployment and optimization of various conversational ML models - Intent/NER/Translation

• Building a central feature store using Redis and Mongo as the underlying stores

• Designing and Implementing Data Pipelines(stream and batch) to push, transform feature data to online stores.

• Building Unified Inferencing Engine for ML models

• Building online store service

• Model Deployment Service

Technologies - Spark on Kubernetes, Kubernetes, Redis, , Mongo , AWS - EKS, EMR, Elastic Cache, Data Pipeline, Kinesis, Dynamo

Deployment tools - Jenkins, Terraform, Helm

Languages - Java, python, Scala

I work in the engineering division of the Data services and Platform -DSP team ( formerly called Enterprise Data Warehousing -EDW) which oversees the Big Data platform and workflows at Expedia. Worked on a project called 'Jetstream' with the following work highlights

• Contributed to building highly available, scalable data lakes for various teams across expedia.

• Built an ingestion system that can replicate hive data across datalakes

• Built a hive data analyzer that smartly finds which hive partitions were updated for replication

• Worked on building systems that enable hive data compression and compaction for better data replication performance

• Built orchestration engine to orchestrate various hive data pipelines.

Big Data technologies used: Hadoop, Hive, HBase, Kafka, Presto, Elasticsearch, DynamoDB

AWS technologies worked on : lambda, data pipeline, EMR, dynamo , API Gateway, Athena, ECS, EC2, S3, SNS

Deployment tools : terraform, Jenkins

Languages: Java, typescript, python