Artificial Intelligence Engineer

Requirements

• 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
• Bachelors/Masters/PhD degree in Statistics, Mathematics or 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 (Open search 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
• Strong communication abilities to explain technical concepts
• Collaborative mindset for cross‑functional team work
• Detail‑oriented with strong focus on quality
• Self‑motivated and able to work independently
• Passion for solving complex search problems

• 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
• 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