Principal Search/AI Engineer

**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.

Essential functions

**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

Qualifications

**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
• Strong 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

Would be a plus

**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, , or Vertex AI Search
• Proven experience implementing agentic AI frameworks (LangGraph, LangChain) and orchestrating mult…