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Mid-level Bangalore role with a generic title, broad GenAI and search requirements increases applicant competition.
Specialized OpenSearch, vector search, and Bedrock GenAI experience creates strong domain bias and limited transferability.
Explicit 5+ years OpenSearch requirement plus mandatory vector search and Bedrock GenAI skills implies strict technical filters.
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Design, deploy, and manage scalable enterprise OpenSearch clusters for eCommerce product discovery and recommendation systems.
Develop and optimize hybrid search architectures combining keyword and AI-powered semantic search including vector search and Retrieval-Augmented Generation (RAG).
Integrate OpenSearch with Amazon Bedrock Foundation Models to build generative AI solutions such as conversational commerce and intelligent product discovery.
5+ years of experience with OpenSearch or Elasticsearch including Query DSL, index lifecycle, and search relevance tuning.
Hands-on experience with vector databases, embedding generation, semantic and hybrid search architectures.
Experience with Amazon Bedrock services and APIs for GenAI integration with OpenSearch.
Strong programming skills in Java, Python, Node.js, or Go; experience building microservices and REST APIs.
Experienced engineer able to design and operate large-scale, AI-enhanced search platforms specifically in eCommerce contexts.
Technically proficient in both traditional search and advanced semantic search including vector search and embedding management.
Practitioner familiar with integrating foundation AI models (Amazon Bedrock) into search workflows and deploying scalable, reliable cloud-native microservices.