8 of 8 Permanent Feature Engineering Jobs in London

deliver production-grade ML systems for a leading digital gaming and gambling platform. This is a hands-on role combining data science expertise with engineering skills - you'll build models, optimise algorithms, and deploy solutions at scale to enhance customer engagement and decisioning. You'll work closely with Data … science & modelling: Develop, validate, and optimise predictive models using advanced ML algorithms (e.g., gradient boosting, logistic regression, ensemble methods). End-to-end ML engineering: Deploy models as APIs, batch jobs, and streaming services; implement CI/CD, monitoring, and rollback strategies. Feature engineering & pipelines: Build scalable ...

Face Transformers. Experience building, training, and deploying machine learning models for classification, regression, clustering, and NLP tasks. Understanding of model evaluation metrics, hyperparameter tuning, feature engineering, and MLOps practices for production deployment. Generative AI & LLM Integration - Proficient in working with Large Language Models including OpenAI GPT models, Anthropic … Claude, Azure OpenAI, and open-source alternatives (Llama, Mistral). Experience with prompt engineering, fine-tuning, RAG (Retrieval Augmented Generation) architectures, vector databases (Pinecone, ChromaDB, FAISS), embeddings, and building AI-powered automation solutions that leverage natural language understanding. Appian BPA Platform - Strong experience with Appian low-code platform including ...

models for user personalisation, including recommendation systems, ranking models, user segmentation, and content understanding, with a strong focus on TensorFlow-based development. Data Pipeline Engineering: Build and maintain scalable data pipelines to support feature engineering and model training across large structured and unstructured datasets, leveraging cloud‐native … relevance. Experimentation: Lead A/B testing and offline experimentation to evaluate model performance and guide ongoing improvement. Cross‐Functional Collaboration: Work closely with engineering, product, data, and research teams to ensure ML solutions align with product and business goals. Research & Innovation: Stay informed on advances in machine learning ...

skills, and a results-driven mindset Role responsibilities: Build, tune, and maintain predictive and ML models using AWS SageMaker Analyse large datasets and perform feature engineering to improve model performance Run experiments, test hypotheses, and optimise models for accuracy and value Monitor model performance and manage retraining over ...

multi-tenant SaaS platform. Create ETL pipelines for large, complex property datasets. Monitor, tune, and iterate on model performance. Collaborate with product and engineering teams on ML-driven features. Document solutions and communicate clearly with technical and non-technical stakeholders. Key Skills 4+ years as a Machine Learning Engineer … similar role. Strong Pythonengineering skills. Experience deploying ML models in production. Understanding of multi-tenant SaaS architecture. Solid ETL, data processing, and feature engineering experience. Strong statistical and analytical capabilities. Experience in start-up or scale-up environments. Desirable MLOps tools (MLflow, Kubeflow, Sagemaker, etc.) Experience in PropTech ...

proof-of-concept development using Databricks notebooks, analytical outputs and machine learning techniques to validate product concepts Own technical product definition, including data structures, feature sets, scoring methodologies, model architectures and delivery formats in collaboration with the Product Team Define and monitor technical success metrics (data coverage, refresh latency … needs into technical product requirements Support rapid prototyping and proof-of-concept development using Databricks notebooks and analytics outputs Define product-level data structures, feature sets, scoring outputs and delivery formats in collaboration with the Product Team Product Launch & Performance responsibilities: Define technical success metrics (data coverage, refresh latency ...

large language models, agentic AI – across research, rankings, submissions and product innovation. Oversee the end-to-end analytics lifecycle, from data acquisition and feature engineering through to model development, validation, deployment and monitoring Ensure AI-driven outputs are explainable, transparent and aligned with Chambers’ standards and reputation ...

model lifecycle. This isn't a traditional "build and hand off" role, you'll manage everything from data collection to feature engineering, model development, testing, and deployment. Key Responsibilities: Develop credit risk predictive models, including scorecards, income models, and early repayment models. Build automation for lending decisions … Create forecasting and marketing models to support customer segmentation and growth strategies. Own the full modelling process, from raw data through deployment, without separate engineering teams. Collaborate with cross-functional teams to ensure models are robust, actionable, and scalable. Continuously improve modelling approaches as volumes and portfolios expand. REQUIREMENTS ...