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.