MNC InsiderMNC Insider

Software Engineer

Caterpillar

Software Engineer

full-timePosted: Aug 27, 2026Updated: Sep 1, 2026Karnataka, Bangalore

Job Description

Career Area:Technology, Digital and DataJob Description:Your Work Shapes the World at Caterpillar Inc. When you join Caterpillar, you're joining a global team who cares not just about the work we do – but also about each other. We are the makers, problem solvers, and future world builders who are creating stronger, more sustainable communities. We don't just talk about progress and innovation here – we make it happen, with our customers, where we work and live. Together, we are building a better world, so we can all enjoy living in it.About the Role 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. Key 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. Configure and optimize: 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. Strong knowledge of: Query DSL Aggregations Index lifecycle management Search relevance tuning Analyzers and tokenizers Sharding and replication OpenSearch Dashboards/Kibana Vector Search & AI Search Hands-on experience with: Vector databases and vector indexing Embedding generation Semantic search Retrieval systems Hybrid search architectures RAG implementations Experience implementing: k-NN ANN algorithms Similarity ranking Embedding lifecycle management Amazon Bedrock & GenAI Experience with Amazon Bedrock services and APIs. Knowledge of: Foundation Models Prompt Engineering Embedding Models Knowledge Bases Agentic AI workflows Experience integrating Bedrock with OpenSearch for semantic retrieval and GenAI applications. Software Development Strong programming skills in: Java Python Node.js Go (preferred) Experience building microservices and REST APIs. Understanding of distributed systems and scalability concepts. Posting Dates:August 27, 2026 - September 2, 2026Caterpillar is an Equal Opportunity Employer. Qualified applicants of any age are encouraged to applyNot ready to apply? Join our Talent Community.

Locations

  • Karnataka, Bangalore

Skills Required

  • OpenSearchintermediate
  • Amazon Bedrock servicesintermediate
  • microservicesintermediate

Required Qualifications

  • OpenSearch & Search Engineering (experience)
  • 5+ years of experience with OpenSearch or Elasticsearch. (experience, 5 years)
  • Strong knowledge of: (experience)
  • Query DSL (experience)
  • Aggregations (experience)
  • Index lifecycle management (experience)
  • Search relevance tuning (experience)
  • Analyzers and tokenizers (experience)
  • Sharding and replication (experience)
  • OpenSearch Dashboards/Kibana (experience)
  • Vector Search & AI Search (experience)
  • Hands-on experience with: (experience)
  • Vector databases and vector indexing (experience)
  • Embedding generation (experience)
  • Semantic search (experience)
  • Retrieval systems (experience)
  • Hybrid search architectures (experience)
  • RAG implementations (experience)
  • Experience implementing: (experience)
  • k-NN (experience)
  • ANN algorithms (experience)
  • Similarity ranking (experience)
  • Embedding lifecycle management (experience)
  • Amazon Bedrock & GenAI (experience)
  • Experience with Amazon Bedrock services and APIs. (experience)
  • Knowledge of: (experience)
  • Foundation Models (experience)
  • Prompt Engineering (experience)
  • Embedding Models (experience)
  • Knowledge Bases (experience)
  • Agentic AI workflows (experience)
  • Experience integrating Bedrock with OpenSearch for semantic retrieval and GenAI applications. (experience)
  • Software Development (experience)
  • Strong programming skills in: (experience)
  • Java (experience)
  • Python (experience)
  • Node.js (experience)
  • Go (preferred) (experience)
  • Experience building microservices and REST APIs. (experience)
  • Understanding of distributed systems and scalability concepts. (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.
  • Configure and optimize:
  • 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.

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