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Role Summary We're looking for a Principal Search/AI Engineer to join our team of search experts and serve as technical lead on large-scale search modernization projects and programs for Fortune-1000 clients. You will collaborate with our established search practice to design, build, and optimize AI-powered search platforms that deliver rich content and product discovery experiences. In this role, you'll drive technical excellence across multi-year digital transformation initiatives, lead solution architecture, and mentor engineering teams while implementing cutting-edge search solutions enhanced with AI and agentic capabilities for our retail, e-commerce, and enterprise clients. Key Responsibilities Search Platform Leadership - Lead technical architecture and strategy for complex search modernization programs spanning multiple years and teams - Drive end-to-end ownership of Lucene-based search platforms (Solr/Elasticsearch/OpenSearch) including schema design, index pipelines, scaling, and monitoring - Architect high-throughput, low-latency Java services that expose search functionality via REST/gRPC APIs - Design and implement advanced search relevance strategies including semantic search, hybrid approaches, and AI-enhanced ranking - Perform sophisticated relevance tuning leveraging BM25, TF-IDF, vector similarity, and learning-to-rank methodologies AI and Agentic Search Innovation - Prototype and productionize next-generation search features including faceted navigation, autocomplete/type-ahead, spell-checking, vector search, and hybrid retrieval - Lead integration of AI-powered conversational search, intelligent query understanding, and agentic workflows to enable natural-language and multi-turn search experiences - Architect and implement agentic AI frameworks (LangGraph, LangChain) for multi-agent search workflows and intelligent content discovery Technical Leadership and Collaboration - Partner with data scientists and ML engineers to deploy and optimize PyTorch-based ranking models for query understanding, embeddings, and re-ranking - Lead design and implementation of data pipelines (Python/Java/Spark/Dataflow) to generate training signals from user behavior analytics - Mentor and guide engineering teams on search best practices, performance optimization, and emerging AI technologies - Drive technical decision-making across cross-functional teams and stakeholder groups Quality and Operational Excellence - Establish comprehensive metrics, alerting, and automated testing frameworks to guarantee SLA compliance at enterprise scale - Lead performance profiling and capacity planning for indexes serving hundreds of millions of documents or SKUs - Drive continuous improvement initiatives for search relevance, performance, and user experience metrics Required Qualifications - 8+ years building enterprise-scale back-end systems in Java (Spring Boot or similar) with demonstrated focus on performance, scalability, and reliability - 5+ years hands-on leadership experience with Lucene-based search engines (Apache Solr, Elasticsearch, or OpenSearch) including advanced index design, relevance tuning, and cluster operations - 3+ years experience as technical lead on large-scale search or platform modernization programs for enterprise clients - Proven track record delivering AI-enhanced search solutions handling 100M+ documents/SKUs at Fortune-1000 scale (e-commerce, media, enterprise content, or similar) - Deep understanding of information retrieval concepts including tokenization, analyzers, BM25, TF-IDF, vector similarity, semantic search, and machine learning integration - Solid proficiency in Python for data manipulation, ML model integration, and prototyping - Extensive experience with cloud-native architectures, Docker/Kubernetes, CI/CD, and enterprise deployment (AWS, GCP, or Azure) - Demonstrated leadership in mentoring engineers and driving technical excellence across teams Preferred / Bonus Skills - Expert-level understanding of Lucene-based search engines and their underlying architecture, performance characteristics, and optimization strategies - Hands-on experience with modern search platform ecosystem including Algolia, Coveo, Lucidworks, Constructor.io, or Vertex AI Search - Proven experience implementing agentic AI frameworks (LangGraph, LangChain) and orchestrating multi-agent search workflows - Advanced experience embedding and serving PyTorch models for Learning-to-Rank, embeddings, conversational AI, and generative re-ranking - Deep knowledge of vector databases and similarity search (OpenSearch/ES K-NN, Milvus, Pinecone, Weaviate, Chroma) - Experience designing streaming data pipelines (Apache Kafka, Pub/Sub, Apache Flink) for real-time indexing and personalization - Active contributions to open-source information retrieval/ML projects, published research, or recognized technical thought leadership - Experience with cloud-native search services (AWS .