Principal Search/AI Engineer

Essential functions

• 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

• 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

• 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 CloudSearch, GCP Vertex AI Search, Azure Cognitive Search) and hybrid architectures

• Background in natural language processing, transformer models, and large language model integration

• Technical marketing and thought leadership experience including creating and publishing blog posts, white papers, presenting at professional conferences, and maintaining active presence on professional social media platforms

We offer

• Opportunity to work on bleeding-edge projects
• Work with a highly motivated and dedicated team
• Competitive salary
• Flexible schedule
• Benefits package - medical insurance, sports
• Corporate social events
• Professional development opportunities
• Well-equipped office

About us

Grid Dynamics (NASDAQ: GDYN) is a leading provider of technology consulting, platform and product engineering, AI, and advanced analytics services. Fusing technical vision with business acumen, we solve the most pressing technical challenges and enable positive business outcomes for enterprise companies undergoing business transformation. A key differentiator for Grid Dynamics is our 8 years of experience and leadership in enterprise AI, supported by profound expertise and ongoing investment in data, analytics, cloud & DevOps, application modernization and customer experience. Founded in 2006, Grid Dynamics is headquartered in Silicon Valley with offices across the Americas, Europe, and India.