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ElasticSearch Architect

ElasticSearch Architect

NexperiaKuala Lumpur, Kuala Lumpur, Malaysia
15 hari lalu
Penerangan pekerjaan
  • Design, plan, and implement company-wide, highly available, and scalable Elasticsearch cluster architectures to meet petabyte-scale data storage and millisecond-level retrieval requirements.
  • Lead the integration and convergence of Elasticsearch with the AI / ML technology stack (e.g., vector database concepts, LLM frameworks), defining the long-term technical roadmap.Utilizing Elasticsearch's ML module or external ML module for
  • anomaly detection
  • , log classification, and business metric forecasting.Developing and optimizing
  • Retrieval-Augmented Generation (RAG)
  • pipelines, using Elasticsearch as an external knowledge base to enhance the capabilities and accuracy of large language models.Deploying and managing Elasticsearch
  • Learning to Rank
  • models to improve the intelligent ranking of search results.
  • Collaborate with data scientists and algorithm engineers to seamlessly deploy and run machine learning models in Elasticsearch production environments
  • Minimum 5 years of hands-on Elasticsearch experience with a deep understanding of its core principles (inverted index, text analysis, sharding, routing, etc.).
  • Proficiency in Elasticsearch index design, mapping optimization, DSL query writing, and performance tuning.
  • Must have :
  • Extensive practical experience with
  • vector search
  • , familiar with embedding models, vector index principles, and performance optimization.
  • Must have :
  • Hands-on experience building and optimizing
  • RAG
  • systems, understanding how to leverage Elasticsearch to enhance LLMs.
  • Familiarity with Elasticsearch's built-in machine learning features (anomaly detection, data frame analysis, etc.) and experience applying them in production environments.
  • Understanding of how to integrate Elasticsearch with external ML workflows (e.g., PyTorch, TensorFlow, Scikit-learn).
  • Proficiency in Java or Python for relevant SDK development and scripting.
  • Familiarity with the entire ELK stack, including Logstash for data ingestion and Kibana for visualization and dashboard development.
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