Search Lead

Indian Mesh is seeking a highly skilled and passionate Search Lead to drive the evolution of our search capabilities and significantly enhance our customer's search experience. The ideal candidate will possess strong computer science fundamentals and extensive expertise in search technologies, information retrieval, and machine learning, with a desire to build and optimize large-scale, high-performance search systems.

Responsibilities:

• Drive the continuous improvement of our customer's search experience, making it a core focus of all efforts.
• Work on distributed infrastructure related to search systems, ensuring scalability and reliability.
• Develop and optimize indexing pipelines for efficient and accurate data processing.
• Architect and build the next generation of Search features and underlying infrastructure.
• Understand, measure, and meticulously debug search ranking quality to identify areas for enhancement.
• Implement strategies and features to significantly improve the quality of our search results and increase conversion rates.
• Apply advanced Information Retrieval and Machine Learning techniques to retrieve highly relevant results, provide optimal rankings, and implement sophisticated scoring features.
• Help us tackle complex new challenges, including advancing discovery mechanisms and personalizing search experiences for individual users.

Required Expertise & Skills:

• Strong Computer Science fundamentals.
• In-depth Lucene expertise.
• Expert-level proficiency in any of Solr, Elastic Search, or Sphinx.
• Proven real-world expertise with Information Retrieval, Machine Learning, and Recommendation Systems.
• Extensive 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 such as Hidden Markov Models and Conditional Random Fields.
• Familiarity with classical statistics and time series analysis.