Software Engineer

Career Area: Technology, Digital and Data

We are looking for a highly skilled OpenSearch Engineer to build and optimize next-generation search experiences for our eCommerce platform. The ideal candidate will possess deep expertise in OpenSearch/Elasticsearch, vector search, semantic retrieval, and Amazon Bedrock-based GenAI solutions.

This role will be responsible for designing scalable search architectures that combine traditional keyword search with AI-powered semantic search, recommendation systems, and Retrieval-Augmented Generation (RAG) capabilities to enhance product discovery and customer experience.

Responsibilities

Search Platform Engineering

• Design, deploy, and manage enterprise-scale OpenSearch clusters.
• Build high-performance search solutions for product discovery, category browsing, filtering, autocomplete, and recommendations.
• Define and maintain index mappings, analyzers, tokenizers, synonyms, and ranking strategies.
• Optimize query performance, relevance scoring, aggregations, and search latency.
• Implement real-time and near real-time indexing pipelines for products, inventory, pricing, promotions, and customer data.

Vector Search Semantic Search

• Design and implement vector indexing strategies using OpenSearch vector engine.
• Build semantic search capabilities using embeddings generated from foundation models.
• Dense vectors
• k-NN search
• Approximate Nearest Neighbor (ANN)
• Hybrid search (keyword + vector search)
• Similarity search
• Implement product recommendations, related products, and personalized search experiences using vector embeddings.
• Evaluate search quality using relevance metrics and semantic retrieval benchmarks.

Generative AI Amazon Bedrock

• Integrate OpenSearch with Amazon Bedrock Foundation Models.
• Develop Retrieval-Augmented Generation (RAG) solutions for conversational commerce and intelligent product discovery.
• Generate embeddings using Bedrock-supported models such as:
• Amazon Titan Embeddings
• Cohere Embed Models
• Anthropic Claude (for AI-assisted search applications)
• Build AI-powered use cases:
• Natural language product search
• Conversational shopping assistants
• Product recommendation systems
• Catalog enrichment and metadata generation
• Optimize embedding pipelines, chunking strategies, and vector retrieval performance.

Platform Operations

• Monitor cluster health, indexing performance, and search analytics.
• Troubleshoot production issues and ensure high availability.
• Implement backup, disaster recovery, and security best practices.
• Automate deployments using CI/CD pipelines and Infrastructure-as-Code.

Cross-functional Collaboration

• Partner with Product, Data Science, Merchandising, and Engineering teams to improve search relevance and conversion rates.
• Conduct performance tuning and capacity planning exercises.
• Drive best practices for search architecture and AI-powered search solutions.

Required Skills

OpenSearch Search Engineering

• 5+ years of experience with OpenSearch or Elasticsearch.
• Query DSL
• Aggregations
• Index lifecycle management
• Search relevance tuning
• Analyzers and tokenizers
• Sharding and replication
• OpenSearch Dashboards/Kibana

Vector Search AI Search

• Vector databases and vector indexing
• Embedding generation
• Semantic search
• Retrieval systems
• Hybrid search architectures
• RAG implementations
• k-NN
• ANN algorithms
• Embedding lifecycle management

Amazon Bedrock GenAI

• Experience with Amazon Bedrock services and APIs.
• Foundation Models
• Prompt Engineering
• Embedding Models
• Knowledge Bases
• Agentic AI workflows
• Experience integrating Bedrock with OpenSearch for semantic retrieval and GenAI applications.

Software Development

• Java
• Python
• Node.js
• Go (preferred)
• Experience building microservices and REST APIs.
• Understanding of distributed systems and scalability concepts.

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.