The company is looking to improve its enterprise search system's accuracy and performance.
Requirements
- In-depth knowledge of relevance measurement, tuning, and modeling.
- Expertise in Python and/or C++ is essential.
- Familiarity with embedding-based search systems will be considered a major advantage.
- Engineering experience with large language models and RAG systems is a plus.
- Knowledge of Search and Recommendation algorithms and infrastructure, including retrieval augmented generation (RAG), semantic search using embeddings, text indexing and retrieval, query understanding, various ranking algorithms etc..
Responsibilities
- Design, develop, and oversee our enterprise search infrastructure, employing a mix of vector databases, full-text search engine, and relational database techniques.
- Understand the user's information needs by developing deep learning-based NLP algorithms to analyze, reformulate and suggest search queries effectively.
- Design, implement, and deploy various ranking algorithms to deliver the most relevant results with the best user experience.
- Evaluate and optimize algorithm accuracy by focusing on key metrics.
- Constantly track and analyze end-to-end system performance, leading improvement initiatives as required.
- Stay informed about the latest industry developments and emerging technologies, aligning our search system with, or advancing it beyond, the industry benchmarks.
Other
- Holds a Masters degree in Computer Science, or a relevant field, PhD a plus.
- 5+ years of experience in search, recommendation or question answering systems.
- Exceptional problem-solving capabilities coupled with meticulous attention to detail.
- Outstanding communication skills to explain complex concepts convincingly to non-technical team members.
- Abilities to contribute individually while functioning effectively as part of a team.
