Expertise in any of Solr / Elastic Search / Sphinx
Real-world expertise with Information Retrieval , Machine Learning , Recommendation Systems
Experience working with large , custom search systems
Exposure to architectural patterns of large , high-scale web applications
Hands-on experience in Information Retrieval , Search Ranking , Search Relevance , Machine Learning or statistical models used in linguistic processing , such as Hidden Markov Models and Conditional Random Fields
Familiar with classical statistics and time series analysis
Education Qualification
B.E / B.Tech (Preferably from Tier1 / Tier2 Colleges).
Job Responsibilities
Desire to improve our customer’s search experience a must
Work on distributed infrastructure
Work on indexing pipelines
Architect and build the next generation of Search features and infrastructure
Understand , measure , and debug ranking quality
Improve the quality of our search results and conversion rate
Apply Information Retrieval and Machine Learning techniques to retrieve relevant results and provide optimal rankings and implement scoring features.
Help us tackle new challenges , like discovery and personalization
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