Search & AI Retrieval Lead at proSapient

You will lead the technical evolution of a knowledge discovery engine, bridging the gap between niche human expertise and intelligent search. By architecting hybrid Graph RAG and GNN systems, you'll enable global investors to surface critical insights from complex, unstructured data, directly impacting high-stakes strategic decisions and multi-million dollar deals.

• Define the future search stack by designing a scalable, cloud-native architecture combining vector search, hybrid retrieval, and LLM re-ranking.
• Design and implement a Knowledge Graph architecture to model complex entities and build extraction pipelines using frontier models like GPT-4 and Gemini.
• Build production-grade retrieval pipelines and feature stores on GCP using BigQuery, ensuring long-term scalability, observability, and cost efficiency.

• 5+ years of experience building and scaling production search or retrieval systems with a focus on semantic search and embeddings.
• Proven track record of designing search architectures from first principles and evaluating emerging technologies for commercial use-cases.
• Expert-level Python skills and experience building production-grade LLM applications on cloud-native infrastructure, preferably Google Cloud Platform.

• Architect a next-generation hybrid Graph RAG + GNN reranker system in direct partnership with product teams at Google Cloud.
• Enjoy a high-impact Staff-level IC position reporting directly to the CTO with 70% hands-on technical ownership and zero internal politics.
• Work with a diverse, remote-first European engineering culture that values low-ego collaboration and provides access to unique, varied data sets.