Search Engineer

Role Overview: As a Search Solutions Architect, your main responsibility will be to design and implement advanced search features using OpenSearch/Elasticsearch. You will create and maintain index templates, mappings, and lifecycle policies to ensure data integrity and scalability. Additionally, you will develop custom query builders and search pipelines to meet complex business requirements. Leveraging AI/ML/LLM for data enrichment and improved search relevance will also be a key aspect of your role. You will lead testing efforts to ensure the reliability and accuracy of search features and automate the deployment and management of search infrastructure. Key Responsibilities: - Design and implement advanced search features using OpenSearch/Elasticsearch - Create and maintain index templates, mappings, and lifecycle policies - Develop custom query builders and search pipelines tailored to complex business requirements - Leverage AI/ML/LLM for data enrichment and improved search relevance - Develop and maintain robust, scalable APIs for search using Python (FastAPI) - Lead testing efforts in integration, regression, and performance testing - Automate deployment and management of search infrastructure - Collaborate closely with data engineers and product teams to deliver high-quality search experiences Qualifications Required: - 5+ years of experience with Elasticsearch or OpenSearch in production environments - Experience with AI/LLM-based search experiences such as RAG, learning to rank, and query understanding - Expert level expertise in Python, with experience building APIs (preferably FastAPI) - Deep understanding of search concepts: analysers, tokenisers, relevance tuning, custom scoring, and query DSL - Experience with index template management, lifecycle policies, and large-scale data modelling - Proficiency in designing and optimising search pipelines and custom query builders - Practical hands-on experience developing software using AI-assisted tools and workflows such as Cursor, Co-pilot, and Windsurf - Strong testing background in integration, regression, and performance testing - DevOps skills in automation, CI/CD, and cloud infrastructure (AWS preferred) - Excellent communication and collaboration skills - Experience with S3, AWS Lambda, and cloud-native architectures (Note: Additional details about the company were not provided in the job description) Role Overview: As a Search Solutions Architect, your main responsibility will be to design and implement advanced search features using OpenSearch/Elasticsearch. You will create and maintain index templates, mappings, and lifecycle policies to ensure data integrity and scalability. Additionally, you will develop custom query builders and search pipelines to meet complex business requirements. Leveraging AI/ML/LLM for data enrichment and improved search relevance will also be a key aspect of your role. You will lead testing efforts to ensure the reliability and accuracy of search features and automate the deployment and management of search infrastructure. Key Responsibilities: - Design and implement advanced search features using OpenSearch/Elasticsearch - Create and maintain index templates, mappings, and lifecycle policies - Develop custom query builders and search pipelines tailored to complex business requirements - Leverage AI/ML/LLM for data enrichment and improved search relevance - Develop and maintain robust, scalable APIs for search using Python (FastAPI) - Lead testing efforts in integration, regression, and performance testing - Automate deployment and management of search infrastructure - Collaborate closely with data engineers and product teams to deliver high-quality search experiences Qualifications Required: - 5+ years of experience with Elasticsearch or OpenSearch in production environments - Experience with AI/LLM-based search experiences such as RAG, learning to rank, and query understanding - Expert level expertise in Python, with experience building APIs (preferably FastAPI) - Deep understanding of search concepts: analysers, tokenisers, relevance tuning, custom scoring, and query DSL - Experience with index template management, lifecycle policies, and large-scale data modelling - Proficiency in designing and optimising search pipelines and custom query builders - Practical hands-on experience developing software using AI-assisted tools and workflows such as Cursor, Co-pilot, and Windsurf - Strong testing background in integration, regression, and performance testing - DevOps skills in automation, CI/CD, and cloud infrastructure (AWS preferred) - Excellent communication and collaboration skills - Experience with S3, AWS Lambda, and cloud-native architectures (Note: Additional details about the company were not provided in the job description)