Machine Learning + DevOps

  • Company name:
  • Industry:
  • Location:
    Chicago, IL
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We were tasked to build a smart search engine to provide fast searches powered by Artificial Intelligence, Machine Learning, Knowledge Graph, and personalized relevance tuning.


  • A search engine is a complex task, time-consuming, and needs proper DevOps integration
  • Setting up ElasticSearch cluster, eContext API for Artificial Intelligence / Machine Learning / Natural Language Processing
  • Fine-tuning on big data set to provide accurate and personalized search results


  • Architect, design and implement ElasticSearch Cluster
  • Setup web crawler and Indexer applications which consume RSS feed from a various content provider like Bing, Google, Yahoo. etc.
  • Design and implement Search-API using Golang which extracts search results from Elasticsearch cluster for our end users
  • Deploy web application with continuous integration using Kubernetes on containerized environment
  • Setup stream and crawl ingestion cluster by using Kafka and RabbitMQ


  • Several hundred users were successfully migrated to new search API within a set timeframe.
  • Enhanced and improved performance of cloud-based infrastructure, reduction in cost, and improved search results.
  • We integrated a big data set powered by Artificial Intelligence, Machine Learning, and Natural Language Processing engines.
  • We reduced load from back end servers to ensure immutable, tamper-proof search results.