At a Glance
- Tasks: Lead the evolution of a knowledge discovery engine and design scalable search architectures.
- Company: Global expert network platform driving multi-million dollar investment decisions.
- Benefits: Competitive salary, hybrid work model, and direct impact on high-stakes projects.
- Other info: Join a diverse, remote-first team that values collaboration and innovation.
- Why this job: Shape the future of AI retrieval and collaborate with top product teams at Google Cloud.
- Qualifications: 5+ years in search systems, expert Python skills, and experience with cloud-native infrastructure.
The predicted salary is between 80000 - 100000 £ per year.
Company Description: Global expert network platform powering multi‑million dollar investment decisions for Private Equity and consulting firms.
Job Description: 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.
Location: London, UK (Hybrid)
Why this role is remarkable:
- 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.
What You Will Do:
- 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.
The ideal candidate:
- 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.
Search & AI Retrieval Lead (£100k) at Global Expert Network Platform employer: Jack & Jill
Join a pioneering global expert network platform in London, where you will play a pivotal role in shaping the future of knowledge discovery and AI retrieval. With a strong emphasis on collaboration and innovation, our hybrid work culture fosters personal growth and offers unique access to diverse data sets, empowering you to make impactful contributions to high-stakes investment decisions. Enjoy a competitive salary and the opportunity to work directly with leading product teams at Google Cloud, all while being part of a supportive, low-ego engineering environment.
StudySmarter Expert Advice🤫
We think this is how you could land Search & AI Retrieval Lead (£100k) at Global Expert Network Platform
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We think you need these skills to ace Search & AI Retrieval Lead (£100k) at Global Expert Network Platform
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
Quantify Your Achievements:Employers love numbers! When drafting your CV, highlight your achievements with quantifiable results. For instance, mention how your data analysis led to a certain percentage increase in efficiency or revenue at a previous job or project. These details can really make your application pop!
Craft a Tailored Cover Letter:For a full-time role at Jack & Jill, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.
Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at Jack & Jill. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
How to prepare for a job interview at Jack & Jill
✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
✨Showcase Your Projects
Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!
✨Get Comfortable with Python and R
Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at Jack & Jill!
✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
