Role overview
Workato transforms technology complexity into business opportunity. As the leader in enterprise orchestration, Workato helps businesses globally streamline operations by connecting data, processes, applications, and experiences. Its AI-powered platform enables teams to navigate complex workflows in real-time, driving efficiency and agility.
Trusted by a community of 400,000 global customers, Workato empowers organizations of every size to unlock new value and lead in today's fast-changing world. Learn how Workato helps businesses of all sizes achieve more at workato.com.
What you'll work on
As we work towards building out the Context Layer for the Agentic Enterprise, we are looking for an exceptional Search/AI Engineer with experience in Search Relevance to join our growing team. In this role, you will lead the design, development, and optimization of intelligent search systems that leverage machine learning at their core. You'll be responsible for building end-to-end retrieval pipelines that incorporate advanced techniques in query understanding, ranking, and entity recognition. The ideal candidate combines deep expertise in information retrieval and search relevance with hands-on experience applying machine learning to real-world search problems at scale.
- Lead the development of advanced query understanding systems that parse natural language, resolve ambiguity, and infer user intent
- Design and deploy learning-to-rank models that optimize relevance using behavioral signals, embeddings, and structured feedback
- Build and scale robust Entity Recognition pipelines that enhance document understanding, enable contextual disambiguation, and support entity-aware retrieval
- Architect next-gen search infrastructure capable of supporting highly dynamic document corpora and real-time indexing
- Create and maintain graph-based knowledge systems that enhance LLM capabilities through structured relationship data
- Drive improvements in query rewriting, intent classification, and semantic search, using both statistical and neural methods
- Own the design of evaluation frameworks for offline/online relevance testing, A/B experimentation, and continual model tuning
- Collaborate with product and applied research teams to translate user needs into data-informed search innovations
- Produce clean, scalable code and influence system architecture and roadmap across the relevance and platform stack
What we're looking for
- Strong communication abilities to explain technical concepts
- Collaborative mindset for cross-functional teamwork
- Detail-oriented with strong focus on quality
- Self-motivated and able to work independently
- Passion for solving complex search problems
*(REQ ID: 2472)