Associate Agentic Search Infrastructure Engineer - Moveworks

ServiceNow•Mountain View, CA

1d•Onsite

About The Position

The Associate Agentic Search Infrastructure Engineer will help build a composable, agent-native search infrastructure that lets agents and applications locate, search, discover, and recommend resources across heterogeneous enterprise data. The role owns infrastructure beneath the agentic search platform, including the index engine, ingestion and enrichment pipeline, and entity, identity, and feature intelligence that gives content context. A key focus is aligning index-time enrichments with agents’ query-time expectations so retrieval, ranking, and reasoning rely on a consistent foundation. The engineer will build and operate hybrid indexing technology at scale, including keyword-based sparse-vector indices, embedding-based semantic indices, and emerging semantic-ID indexing. The role owns the index lifecycle and provides unified contracts to the search primitives and recipes that agents use. It also designs high-throughput ingestion and enrichment pipelines to create multi-representation, permission-aware content, orchestrating parsing, chunking, embedding, entity resolution, and access control. The position includes multi-modal indexing across enterprise data such as text, structured content, diagrams, images, audio, and video, with a unified retrieval surface. The engineer will contribute to a knowledge graph that resolves entities across enterprise data sources, and improve search performance, scalability, and observability—including query latency, indexing throughput, and cluster health. Techniques such as vector quantization may be used to balance model quality, retrieval performance, and storage cost. The role owns search infrastructure end to end, from intake and design through rollout and ongoing operations, and helps shape the search roadmap as the platform expands to support new agents, primitives, recipes, and emerging needs. It drives reliability and operability through index lifecycle policies, capacity planning, and cluster scaling strategies. This is a highly collaborative, in-person position working with Search Ranking, ML, and Platform engineering teams.

Requirements

BS or MS in Computer Science, or an equivalent engineering background.
Hands-on experience with search technologies.
Proficiency in one or more search technologies such as Lucene, Solr, Elasticsearch, or OpenSearch.
Willingness and enthusiasm to participate in an in-person team culture and collaborate on-site with colleagues every day.

Nice To Haves

PhD.
Experience with vector databases.

Responsibilities

Build and own infrastructure for the agentic search platform, including the index engine, ingestion and enrichment pipeline, and entity, identity, and feature intelligence.
Keep index-time enrichments aligned with query-time expectations to provide a consistent foundation for retrieval, ranking, and reasoning.
Build and operate hybrid indexing technology at scale, including keyword-based sparse-vector indices, embedding-based semantic indices, and semantic-ID indexing.
Own the full index lifecycle and expose unified contracts to search primitives and agent-composed recipes.
Design and operate high-throughput ingestion and enrichment pipelines that create a multi-representation, permission-aware corpus.
Orchestrate content processing stages such as parsing, chunking, embedding, entity resolution, and access control.
Build and operate multi-modal indexing for text, structured content, diagrams, images, audio, and video, enabling unified retrieval across forms and sources.
Contribute to a knowledge graph that resolves entities across enterprise data sources and links resources through canonical entity relationships.
Improve search performance, scalability, and observability, including query latency, indexing throughput, and cluster health.
Apply techniques such as vector quantization to balance model quality, retrieval performance, and storage cost.
Own search infrastructure from intake and design through rollout and ongoing operations, and help shape the search roadmap.
Drive platform reliability and operability through index lifecycle policies, capacity planning, and cluster scaling strategies.
Collaborate in person with Search Ranking, ML, and Platform engineering teams.