- Design, develop, and own end-to-end search architecture, including content ingestion, indexing, retrieval, ranking, and query processing.
- Lead the development of next-generation AI search capabilities, including Vector Search, Semantic Search, Hybrid Search, Retrieval-Augmented Generation (RAG), and MCP-based integrations.
- Define and drive the technical roadmap for search and retrieval platforms in collaboration with Product Management and Engineering Leadership.
- Architect, optimize, and maintain large-scale OpenSearch/Elasticsearch clusters to ensure high performance, scalability, reliability, and operational efficiency.
- Build and enhance data pipelines for content ingestion, enrichment, metadata extraction, embedding generation, and indexing.
- Develop search relevance strategies, ranking models, and retrieval mechanisms to improve search quality and user experience.
- Establish engineering best practices around search infrastructure, observability, monitoring, performance tuning, security, and operational excellence.
- Mentor and guide engineers on search technologies, distributed systems, system design, and AI-powered retrieval architectures.
- Ensure platform SLAs/SLOs for latency, throughput, availability, scalability, and data freshness are consistently met.
- Collaborate with cross-functional teams to integrate search and AI capabilities across multiple enterprise products and applications.
- Evaluate emerging search, AI, and retrieval technologies and drive their adoption where appropriate.
Required Skills & Experience




- Strong hands-on experience with OpenSearch or Elasticsearch in large-scale production environments.
- Deep understanding of Information Retrieval (IR), search relevance, indexing, ranking, query processing, and distributed search architectures.
- Experience designing and implementing Vector Search, Semantic Search, Hybrid Search, and Retrieval-Augmented Generation (RAG) solutions.
- Strong understanding of embeddings, vector databases, LLM-powered applications, and modern AI search architectures.
- Experience building scalable data ingestion, transformation, and indexing pipelines.
- Expertise in designing cloud-native, highly available, and scalable distributed systems.
- Experience working with AWS services and cloud-based architectures.
- Robust programming skills in Java, Python, Node.js, or similar backend technologies.
- Experience with observability, monitoring, capacity planning, and performance optimization of large-scale systems.
- Excellent problem-solving, communication, and technical leadership skills.

Preferred Qualifications
- Experience with Learning-to-Rank (LTR), search analytics, and relevance tuning.
- Familiarity with machine learning models used for search ranking and retrieval optimization.
- Experience with Kubernetes, Docker, CI/CD pipelines, and microservices architectures.
- Knowledge of enterprise search platforms and large-scale content management systems.
- Experience leading technical initiatives and mentoring engineering teams.

📌 OpenSearch Elasticsearch Search Architecture Enterprise Search (Chandigarh)
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📍 Chandigarh