Query Reformulation and Document Embeddings

Rahul Raja is a Staff Software Engineer at LinkedIn, working on large-scale search infrastructure, information retrieval systems, and integrating AI/ML to improve ranking and semantic search experiences.

The Future of Information Retrieval: From Dense Vectors to Cognitive Search // MLOps Podcast #362 with Rahul Raja, Staff Software Engineer at LinkedIn

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// Abstract

Information Retrieval is evolving from keyword matching to intelligent, vector-based understanding. In this talk, Rahul Raja explores how dense retrieval, vector databases, and hybrid search systems are redefining how modern AI retrieves, ranks, and reasons over information. He discusses how retrieval now powers large language models through Retrieval-Augmented Generation (RAG) and the new MLOps challenges that arise, embedding drift, continuous evaluation, and large-scale vector maintenance.

Looking ahead, the session envisions a future of Cognitive Search, where retrieval systems move beyond recall to genuine reasoning, contextual understanding, and multimodal awareness. Listeners will gain insight into how the next generation of retrieval will bridge semantics, scalability, and intelligence, powering everything from search and recommendations to generative AI.

// BioRahul is a Staff Engineer at LinkedIn, where he focuses on search and deployment systems at scale. Rahul is a graduate from Carnegie Mellon University and has a strong background in building reliable, high-performance infrastructure. He has led many initiatives to improve search relevance and streamline ML deployment workflows.

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Website: https://www.linkedin.com/

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Timestamps:

[00:00] Vector Search for Media

[00:33] RAG and Search Evolution

[04:45] Cognitive vs Semantic Search

[08:26] High Value Search Signals

[16:43] Scaling with Embeddings

[22:37] BM25 Benchmark Bias

[29:00] Video Search Use Cases

[31:21] Context and Search Tradeoff

[35:04] Personal Memory Augmentation

[39:03] Future of Cognitive Search

[44:51] Access Control in Vectors

[49:14] Search Ranking Challenge

[54:43] Hard Search Problems Solved

[58:29] Freshness vs Cost

[1:02:12] Wrap up