Staff AI Engineer

Навыки

• AI-агенты
• Распределённые системы
• Elasticsearch
• GMV / MRR
• LLM
• Machine Learning
• NLP

О компании и продукте

• Workato delivers enterprise infrastructure for the agentic era, redefining iPaaS and helping enterprises unify data, applications, processes, and AI into a single, governed platform. A leader in Enterprise MCP and trusted by 50% of the Fortune 500, Workato’s cloud-native architecture connects every application, data source, and process to power real-time orchestration at scale. With enterprise-grade security and continuous innovation at its core, Workato provides the trusted foundation for organizations to automate with confidence and operationalize AI across the business. To learn more, visit www.workato.com
• Ultimately, Workato believes in fostering a flexible, trust-oriented culture that empowers everyone to take full ownership of their roles . We are driven by innovation and looking for team players who want to actively build our company.
• But, we also believe in balancing productivity with self-care . That’s why we offer all of our employees a vibrant and dynamic work environment along with a multitude of benefits they can enjoy inside and outside of their work lives.
• If this sounds right up your alley, please submit an application. We look forward to getting to know you!

Задачи

• 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
• In this role, y ou will also be responsible for
• 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

Требования

• Qualifications / Experience / Technical Skills
• Bachelor's/Master's/PhD degree in Statistics, Mathematics, Computer Science, or another quantitative field
• 7+ years of backend engineering experience with 3+ years in search, information retrieval, or related fields
• Strong proficiency in Python
• Hands-on experience with search engines (Opensearch or Elasticsearch)
• Strong understanding of information retrieval concepts spanning traditional methods (TF-IDF, BM25) and modern neural search techniques (vector embeddings, transformer models)
• Experience with text processing, NLP, and relevance tuning
• Experience with relevance evaluation metrics (NDCG, MRR, MAP)
• Experience with large-scale distributed systems
• Proficiency in Knowledge Graph construction and optimization is a plus
• Strong analytical and problem-solving skills
• Soft Skills / Personal Characteristics
• 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)
• 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

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