Senior Staff Agentic Search Infrastructure Engineer - Moveworks

About the Role: Join us as we build the next generation of enterprise search infrastructure powering AI agents. Our mission is to evolve from traditional product-specific search systems into a composable, agent-native platform that enables agents and applications to discover, retrieve, reason over, and recommend information across heterogeneous enterprise data sources. As a Senior Staff Engineer, you will be the technical owner for foundational search infrastructure that serves as the backbone of our agentic platform. You will drive the architecture, scalability, and evolution of core indexing and retrieval systems while partnering closely with Search Ranking, ML, Platform, and AI teams to deliver production-grade search capabilities that power agent experiences across the company.

What You'll Do Own Core Search Infrastructure: Lead the design, development, and operation of critical search infrastructure components, including indexing systems, ingestion pipelines, metadata enrichment frameworks, and retrieval foundations that support a growing ecosystem of AI agents. Drive technical strategy and architectural decisions across multiple search infrastructure domains, ensuring the platform scales to support future agent use cases and expanding enterprise datasets. Serve as the technical authority for the end-to-end search lifecycle, from content ingestion through retrieval and ranking.

Build Next-Generation Indexing Systems: Design and operate large-scale hybrid indexing architectures that combine sparse retrieval, dense vector search, semantic retrieval, and emerging indexing approaches. Establish clean abstractions and interfaces that allow downstream ranking systems and AI agents to leverage search capabilities consistently across multiple retrieval paradigms. Drive innovations that improve search quality, latency, scalability, and cost efficiency across billions of indexed entities.

Lead Enterprise Data Intelligence: Architect high-throughput ingestion and enrichment pipelines that transform enterprise content into rich, permission-aware representations optimized for agent consumption. Define strategies for content parsing, chunking, embedding generation, entity extraction, metadata enrichment, and access control enforcement. Ensure consistency between index-time representations and query-time retrieval mechanisms to provide reliable foundations for agent reasoning. Advance Multi-Modal SearchLead the evolution of indexing and retrieval systems across diverse enterprise content types, including text, structured data, images, diagrams, audio, and video. Develop unified frameworks that enable agents to discover and reason across modalities through a consistent retrieval experience. Partner with ML and AI teams to bring emerging multi-modal capabilities into production.

Drive Knowledge and Entity Infrastructure: Contribute to the design and evolution of enterprise knowledge graph capabilities that connect people, content, tickets, documents, conversations, and business entities. Lead initiatives that improve entity resolution, relationship modeling, and contextual understanding throughout the search platform. Enable richer retrieval and agent reasoning through high-quality enterprise knowledge representations.

Scale Reliability and Performance: Own key platform metrics including search latency, indexing throughput, retrieval quality, infrastructure efficiency, and cluster health. Drive operational excellence through observability, capacity planning, resiliency engineering, and lifecycle management. Lead efforts to optimize infrastructure costs while maintaining high-performance retrieval experiences at scale.

Technical Leadership: Provide technical leadership and mentorship for engineers across the organization. Influence roadmap and architectural direction through technical vision, design reviews, and cross-functional collaboration. Drive alignment across Search Infrastructure, Ranking, ML, and Platform teams to deliver cohesive solutions to complex engineering challenges. Lead multi-quarter initiatives that span teams and materially advance the capabilities of the agentic search platform.

Qualifications: Qualifications RequiredBS or MS in Computer Science or a related technical field; PhD is a plus.8-12+ years of industry experience building large-scale distributed systems, search platforms, or data infrastructure. Deep hands-on experience with search technologies such as Lucene, Elasticsearch, OpenSearch, Solr, or comparable retrieval systems. Strong experience designing and operating distributed systems at scale, including storage, indexing, serving, and data processing infrastructure. Experience building and scaling production systems that leverage vector search, embeddings, retrieval systems, or AI-driven search capabilities. Proven track record of leading complex technical initiatives across teams and influencing architectural decisions beyond an individual team's scope. Demonstrated ability to balance technical depth with pragmatic delivery and operational excellence. For positions in this location, we offer a base pay of $190,900 - $334,100, plus equity (when applicable), variable/incentive compensation and benefits. Sales positions generally offer a competitive On Target Earnings (OTE) incentive compensation structure. Please note that the base pay shown is a guideline, and individual total compensation will vary based on factors such as qualifications, skill level, competencies and work location. We also offer health plans, including flexible spending accounts, a 401(k) Plan with company match, ESPP, matching donations, a flexible time away plan and family leave programs. Compensation is based on the geographic location in which the role is located and is subject to change based on work location.

Preferred: Experience with information retrieval, ranking systems, recommendation systems, or AI-powered search platforms. Experience with knowledge graphs, entity resolution systems, or enterprise data modeling. Familiarity with modern retrieval approaches including dense retrieval, RAG architectures, hybrid search, and semantic indexing. Experience operating large-scale search clusters and performance-sensitive distributed infrastructure. Background working closely with ML or AI platform teams to productionize intelligent retrieval systems.

Culture: Passion for collaborating in a highly engaged, in-person engineering culture. Strong communication skills and a track record of influencing across organizational boundaries. Excitement for solving foundational infrastructure problems that unlock the next generation of AI agent experiences.