This is a job that Jill, our AI Recruiter, is recruiting for on behalf of one of our customers. She will pick the best candidates from Jack's network. The next step is to speak to Jack. Job Title Search & AI Retrieval Lead Salary £100k Company Description proSapient is a market intelligence platform that enables consulting and private equity firms to discover niche industry knowledge. By combining advanced software with a global network of experts, proSapient helps major investors make critical decisions through curated interviews, surveys, and expansive knowledge management tools powered by sophisticated search and AI architecture.

Join proSapient as a Staff-level IC to define the future of information discovery. You will architect a next-generation hybrid Graph RAG and GNN reranker system, partnering closely with Google's product teams. This high-impact role balances hands-on Python development with strategic architectural guidance to surface insights across massive, unstructured datasets for global investors. Location London, UK Why this role is remarkable

  • Build a cutting-edge hybrid Graph RAG + GNN reranker system in direct partnership with Google's product teams, ensuring you stay at the absolute frontier of retrieval technology.
  • Join a high-motivation, low-ego environment with 0% politics where you report directly to the CTO as a Staff-level individual contributor with 70% hands-on work.
  • Work with genuinely unique data spanning every imaginable business sector, from deep tech to agriculture, creating systems that directly impact multi-million dollar investment decisions. What You Will Do
  • Architect and implement a scalable, cloud-native search stack on GCP, deciding between and combining vector search, hybrid retrieval, and graph-based approaches.
  • Design and implement a Knowledge Graph architecture that models complex entities and relationships, integrating extraction pipelines powered by frontier models like OpenAI and Gemini.
  • Develop production-grade intelligent retrieval systems and recommendation engines that leverage both structured and unstructured data to surface the world's most niche expertise. The ideal candidate
  • Possesses 5+ years of experience building and scaling production search or retrieval systems, with a strong background in Python and cloud-native architecture on GCP.
  • Demonstrates deep expertise in semantic search, embeddings-based retrieval, and building production-grade data pipelines on top of frontier LLMs like OpenAI and Gemini.
  • Proven ability to lead technical initiatives and design complex architectures, with specific experience in knowledge graphs, entity linking, and re-ranking strategies at scale. Who are Jack & Jill? Ok, I'll go first. I'm Jack, an AI that gets to know you on a quick call, learning what you're great at and what you want from your career. Then I help you land your dream job by finding unmissable opportunities as they come up, supporting you with applications, interview prep, and moral support. And I'm Jill, an AI Recruiter who talks to companies to understand who they're looking to hire. Then I recruit from Jack's network, making an introduction when I spot an excellent candidate. How does this work?
  • Jack's an AI agent for job searching and career coaching. He works for you.
  • Jill is the AI recruiter working for the company. She recruits from Jack's network.
  • If it's a match and the company wants to meet you, they'll make the intro. In the meantime, if you'd like, Jack will send you excellent alternatives. We never post fake jobs This isn't a trick. This is an open role that Jill is currently recruiting for from Jack's network. Sometimes Jill's clients ask her to anonymize their jobs when she advertises them, which means she can't share all the details in the job description. We appreciate this can make them look a bit suspect, but there isn't much we can do about it. Give Jack a spin! You could land this role. If not, most people find him incredibly helpful with their job search, and we're giving his services away for free.

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