3 of 3 Permanent Feature Engineering Jobs in the City of London

capable of reasoning and planning. Expertly handle LLM fine-tuning (PEFT, LoRA, QLoRA) and RAG pipelines. Data Orchestration: Build ETL/ELT pipelines and feature engineering workflows to integrate structured and unstructured data into centralized platforms. End-to-End MLOps: Own the lifecycle—from CI/CD automation … every system is trustworthy, fair, and explainable, implementing quantifiable metrics for bias detection and regulatory compliance. Technical Toolkit Models: LLMs, Generative AI, Agentic workflows. Engineering: PEFT, Vector Databases (Pinecone/Milvus/Weaviate), Prompt Engineering. Ops: Docker, Kubernetes, CI/CD, Experiment Tracking (MLflow/W&B). Data ...

user experience. What you'll be doing: → Design, train and optimise ML models for personalisation and recommendations → Build and maintain scalable data pipelines for feature engineering and model training → Deploy and monitor models in production — performance, availability, relevance → Lead A/B testing and offline experiments to drive ...

experimentation strategy for the product area — designing A/B tests and multi-armed bandit experiments that generate genuine learning about AI feature performance, not just validate existing assumptions Drive the feedback loop strategy — defining how member behaviour is captured, modelled, and fed back into AI ranking systems … context, using results to change direction rather than confirm instinct Technically credible AI and ML fluency — able to read a model card, challenge a feature engineering decision, write testable acceptance criteria for ML features, and explain confidence intervals to non-technical stakeholders, without overstepping into engineering decisions ...