Personalised Search

4 days ago

Greater London, United Kingdom Swap Full time

Personalised Search & Retrieval Engineer (Agentic Commerce) London Company Say hello to the ecommerce OS. Swap is a leading software provider dedicated to empowering e-commerce brands with innovative, data‑driven solutions. Our cutting‑edge platform helps online retailers optimise their operations, enhance customer experiences, and drive growth. We are committed to fostering a collaborative and inclusive work environment where creativity and innovation thrive. We are seeking a Personalised Search & Retrieval Engineer to own and optimise the core search infrastructure that powers personalised AI experiences. You'll be responsible for the entire search pipeline from semantic understanding to relevance ranking, building systems that deliver contextually relevant results whilst learning from user interactions to continuously improve search quality. What You'll Do Own Semantic Search Quality: Design and optimise embedding models, chunking strategies, and indexing approaches to ensure high-quality semantic understanding and retrieval across diverse content types Build Personalisation Systems: Integrate user-level signals, behavioural patterns, and preference data into search algorithms to deliver contextually relevant and personalised results Design Reranking & Relevance Models: Combine BM25, dense retrieval, and learning‑to‑rank approaches. Build sophisticated reranking algorithms that incorporate relevance signals, user context, and business objectives to optimise search result ordering Create Evaluation Frameworks: Develop comprehensive evaluation systems including offline metrics, online A/B testing, and user satisfaction measurements to continuously assess and improve search performance Optimise Performance & Scale: Tune latency and throughput across the entire search pipeline, working with vector databases and distributed systems to ensure sub‑second response times Build Agentic Integrations: Design retrieval systems that provide accurate context for AI applications, ensuring responses are well‑grounded in relevant, up‑to‑date information Implement Continuous Learning: Create feedback loops that capture user interactions, click‑through rates, and engagement signals to continuously refine personalisation algorithms Skills & Qualifications 4+ years of experience in search engineering, information retrieval, with proven track record of improving search quality at scale Deep expertise in semantic search technologies including embedding models, vector databases (postgres, pgvector), and modern retrieval architectures Strong knowledge of search fundamentals: BM25, TF‑IDF, learning‑to‑rank algorithms, and experience with search evaluation metrics (NDCG, MRR, MAP) Hands‑on experience with machine learning frameworks (PyTorch, TensorFlow), search libraries, and modern ML infrastructure Proficiency in Python, SQL, and distributed computing with experience building high‑throughput, low‑latency systems Experience with personalisation techniques, collaborative filtering, and integrating user behaviour signals into ranking algorithms Knowledge of A/B testing methodologies, statistical analysis, and search analytics for continuous optimisation Familiarity with modern AI/ML operations, model deployment, and monitoring in production environments #J-18808-Ljbffr

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