Verified Job
Engineering - Architecture
Search Engineers
Engineering - Architecture
#746208
Remote / WFH
Slikk · Bengaluru, Karnataka ·
Job Description
Job description
Responsibilities:
• Search Infrastructure Ownership: Own the search platform end-to-end, including: Indexing and data modeling, Query understanding and interpretation, Retrieval mechanisms, ML-powered ranking systems, Result serving infrastructure.
• Real-Time Indexing and Data Pipelines: Develop and maintain real-time indexing pipelines to synchronize: Inventory updates, Pricing changes, Product catalogue modifications from dark stores. Ensure search indices remain fresh and accurate to support quick-commerce operations.
• Ranking and Personalization: Design and optimize ranking algorithms that incorporate: Product relevance, User personalization signals, Inventory availability, Business and merchandising rules. Continuously improve ranking quality to maximize engagement and conversion.
• Discovery Experience: Build and enhance discovery capabilities beyond traditional search, including Personalized feeds, Category ranking systems, Similar product recommendations, "Complete the Look" experiences
• Experimentation and Performance Optimization: Implement A/B testing frameworks to evaluate and improve search experiences. Define, monitor, and optimize key search quality metrics, including MRR (Mean Reciprocal Rank), NDCG (Normalized Discounted Cumulative Gain), CTR (Click-Through Rate), and Conversion Rate. Optimize systems to maintain P95 latency below 200 milliseconds.
Requirements:
• Bachelor's or Master's degree in Computer Science, Engineering, or a related field.
• 5-7 years of experience building production-grade Search/Information Retrieval (IR) systems, preferably in e-commerce environments.
• Strong expertise in Elasticsearch/OpenSearch, including: Index design, Custom analyzers, Query DSL, and Performance tuning and optimization.
• Hands-on experience with Machine Learning ranking techniques, such as Learning-to-Rank (LTR), XGBoost, LightGBM, Feature engineering.
• Experience with embedding-based retrieval systems, including: FAISS, HNSW.
• Strong programming proficiency in Python.
• Solid understanding of Natural Language Processing (NLP) concepts for query understanding, including: Tokenization, Entity extraction, and Intent classification.
• Experience working with real-time indexing technologies, such as Kafka and CDC (Change Data Capture).
• Familiarity with search quality experimentation and evaluation frameworks.
Responsibilities:
• Search Infrastructure Ownership: Own the search platform end-to-end, including: Indexing and data modeling, Query understanding and interpretation, Retrieval mechanisms, ML-powered ranking systems, Result serving infrastructure.
• Real-Time Indexing and Data Pipelines: Develop and maintain real-time indexing pipelines to synchronize: Inventory updates, Pricing changes, Product catalogue modifications from dark stores. Ensure search indices remain fresh and accurate to support quick-commerce operations.
• Ranking and Personalization: Design and optimize ranking algorithms that incorporate: Product relevance, User personalization signals, Inventory availability, Business and merchandising rules. Continuously improve ranking quality to maximize engagement and conversion.
• Discovery Experience: Build and enhance discovery capabilities beyond traditional search, including Personalized feeds, Category ranking systems, Similar product recommendations, "Complete the Look" experiences
• Experimentation and Performance Optimization: Implement A/B testing frameworks to evaluate and improve search experiences. Define, monitor, and optimize key search quality metrics, including MRR (Mean Reciprocal Rank), NDCG (Normalized Discounted Cumulative Gain), CTR (Click-Through Rate), and Conversion Rate. Optimize systems to maintain P95 latency below 200 milliseconds.
Requirements:
• Bachelor's or Master's degree in Computer Science, Engineering, or a related field.
• 5-7 years of experience building production-grade Search/Information Retrieval (IR) systems, preferably in e-commerce environments.
• Strong expertise in Elasticsearch/OpenSearch, including: Index design, Custom analyzers, Query DSL, and Performance tuning and optimization.
• Hands-on experience with Machine Learning ranking techniques, such as Learning-to-Rank (LTR), XGBoost, LightGBM, Feature engineering.
• Experience with embedding-based retrieval systems, including: FAISS, HNSW.
• Strong programming proficiency in Python.
• Solid understanding of Natural Language Processing (NLP) concepts for query understanding, including: Tokenization, Entity extraction, and Intent classification.
• Experience working with real-time indexing technologies, such as Kafka and CDC (Change Data Capture).
• Familiarity with search quality experimentation and evaluation frameworks.
