Bharat Gaurav Bhate
Verified Expert in Engineering
Search Engineer and Software Developer
New York, NY, United States
Toptal member since April 18, 2025
Bharat is a senior search engineer with six years of hands-on experience in Apache Solr, Java, AI/ML, Elastic, and dense vector search. At Lululemon, Bharat led the development of a hybrid semantic search platform integrating Solr with Faiss, Hugging Face, and learning to rank (LTR) to deliver context-aware, highly relevant results. He enriched queries using NLP, built custom Java APIs, and fine-tuned ranking models with behavioral signals, boosting search speed, relevance, and intelligence.
Portfolio
Experience
- Elastic - 5 years
- APIs - 5 years
- Java - 5 years
- Apache Solr - 4 years
- Kibana - 4 years
- Python - 3 years
- Logstash - 2 years
- Grafana - 1 year
Availability
Preferred Environment
MacOS, Linux, Apache Solr
The most amazing...
...feature I developed in my recent project at Lululemon was a hybrid semantic search system that combined Apache Solr with dense vector search.
Work Experience
Senior Search Engineer
Lululemon Athletica
- Developed a high-impact hybrid semantic search system at Lululemon by integrating Apache Solr with dense vector search powered by Faiss and Hugging Face Transformers.
- Built a dual-indexing retrieval pipeline supporting text-based and vector-based search, and applied learning-to-rank (LTR) models to blend results using user behavior signals such as clicks and conversions.
- Pushed Solr beyond its standard capabilities by integrating AI and machine learning tools to deliver a smarter, more responsive search experience.
Search Engineer
Shopify
- Engineered a high-performance help documentation search solution for Shopify, deploying a hybrid architecture on Google Cloud Platform (GCP) using Solr and Elasticsearch. Integrated semantic and vector search to enhance relevance and user experience.
- Implemented Solr for structured search and faceted navigation while leveraging Elasticsearch's vector search with the k-nearest neighbors algorithm (k-NN) and cosine similarity to enable enhanced semantic understanding and similarity-based retrieval.
- Leveraged Solr's faceting and filtering capabilities to enable real-time navigation through documentation, allowing users to filter by article type, category, and role.
Software Engineer
Deloitte
- Implemented advanced search functionalities, including fuzzy, semantic, and vector search, to deliver precise and relevant insights, driving significant improvements in user satisfaction.
- Developed robust back-end services in Python to optimize operations and ensure seamless integration with Elasticsearch.
- Optimized cloud resource management on AWS, utilizing services like Amazon Elastic Compute Cloud (Amazon EC2), Amazon S3, and AWS Lambda to ensure scalability and maximize cost-efficiency.
- Implemented IaC practices using AWS CloudFormation and Terraform to automate resource provisioning, streamlining management and deployment processes as an AWS Certified Professional.
Experience
Hybrid Search Platform for Lululemon
I integrated dense vector search using embeddings generated by Hugging Face Transformers and managed with Solr's KnnVectorField, enabling personalized product ranking and semantic understanding of user queries. I implemented learning to rank (LTR) in Solr, blending traditional and vector-based relevance scoring. I created unified API layers to merge and rank results from both Solr and Elasticsearch, driving improved search precision and user engagement.
Skills
Libraries/APIs
TensorFlow, Hugging Face Transformers
Tools
Apache Solr, Elastic, Logstash, Grafana, Kibana, Apache NiFi
Languages
Java, Python, SQL, Artificial Intelligence Markup Language (AIML)
Storage
NoSQL, Elasticsearch
Frameworks
Apache Spark
Platforms
MacOS, Linux, Azure, Amazon Web Services (AWS), Apache Kafka
Other
APIs, Vectors, Dense Vectors, Data Enrichment, API Integration, Database Schema Design, Indexing, Natural Language Processing (NLP), Learning to Rank (LTR), Vector Search, Shell Scripting
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