9 of 9 Remote/Hybrid Feature Engineering Jobs in London

building, and deploying scalable machine learning solutions to enable advanced analytics, automation, and decision-making across complex government systems. This is a hands-on engineering role, working closely with data scientists, analysts, architects, and product teams to productionise models, build data pipelines, and embed ML capabilities into live services. … Synapse) Experience in AWS environments is beneficial Experience with MLOps practices and tools (CI/CD pipelines, model monitoring, versioning) Strong understanding of data engineering concepts (ETL pipelines, data lakes, feature engineering) Experience working with large-scale structured and unstructured datasets Knowledge of AI ethics, bias mitigation ...

hire has very meaningful impact.They are looking for a Senior Data Scientist to lead the predictive analytics capability and their critical programme.You’ll design feature engineering pipelines, select and validate classification and regression models, and establish rigorous evaluation methodology (targeting AUC-ROC 0.85) across a 300M+ point education … explainable and production-ready.Core Responsibilities: Predictive Model Development : Build and validate supervised learning models (classification & regression) using complex, multi-source, time-series-influenced data. Feature Engineering : Design scalable feature pipelines across attendance, behaviour, wellbeing, assessment and SEND datasets. Evaluation & Methodology : Establish rigorous evaluation frameworks including ...

Negotiable + Benefits The day to day: As a Analytics & Data Engineer within the Data Team, you will sit at the intersection of data engineering and analytics—designing robust, scalable data foundations while generating insights that support operational and strategic decision‐making. The role provides end‐to‐end ownership … pipelines and platforms, and to analyse and interpret data to generate insights, reports, and recommendations that deliver business value. The day to day: Data Engineering: Design, build, and maintain ETL/ELT pipelines for ingesting, transforming, and storing data from multiple sources. Ensure data quality, integrity, and reliability through ...

/ML features, from data ingestion and training to deployment, MLOps workflows, CI/CD, and model versioning Develop LLM/GenAI solutions: prompt engineering, RAG pipelines, embeddings, vector search, and inference optimisation (LoRA/PEFT, quantisation, GPU/TPU) Own observability across data, models, and prompts … Responsible AI practices throughout What you'll need Hands-on LLM/GenAI experience (Gemini or open source) including fine-tuning, RAG pipelines, prompt engineering, and graph-based workflows (ADK, MCP) Strong Python, API/microservices development, GCP (Vertex AI, BigQuery), CI/CD, and containerisation (Docker, Kubernetes) Nice ...

governance frameworks to ensure responsible AI/ML model deployment. Use Python to automate processes and optimise workflows. Handle large datasets, including data preprocessing, feature engineering, and model evaluation. Lead the agile development of AI solutions from concept through deployment and ongoing improvement. Create and maintain comprehensive technical … documentation. Communicate complex AI concepts clearly to stakeholders at all levels. What you'll bring: Proven experience (3 years) in AI solution engineering, with a focus on large language models, prompt engineering, and RAG implementations. Strong skills in data analytics, MLOps practices, and API development. Knowledge of governance ...

Collaborate with data engineers, analysts, and product teams to translate business needs into AI-driven solutions Contribute to the development of data pipelines and feature engineering workflows Integrate models into production using APIs, batch jobs, or real-time systems Apply best practices around experimentation, evaluation, versioning, and monitoring ...

problem definition through to deployment in live environments. * Design, build, and deploy machine learning models within Pega-based decisioning platforms. * Lead model development across feature engineering, training, validation, and ongoing performance monitoring. * Work closely with application and logic developers to embed models into operational decision workflows. * Develop statistical ...

topics, including: Fundamentals of ML and AI (including Machine Learning Lifecycle, Machine Learning Mathematical Foundations); Solving Business Problems with Machine Learning; Data Preparation and Feature Engineering; Planning and preparing ML/AI solution; Data Ethics and Responsible AI; Model Engineering and Training; Model Evaluation and Deployment; Monitoring ...

Function focused on embedding AI into the core of how products are designed, engineered, delivered, and supported. This is not a traditional 'AI feature engineering' role. You'll help shape AI-native ways of working across the business - designing agentic workflows, integrations, and orchestration layers that connect teams … systems, and stages of the product lifecycle. The focus is on building intelligent systems that reduce friction, surface context, automate workflows, and improve how engineering, QA, delivery, sales, and product teams operate together. What you'll be doing: Designing and building agentic AI workflows across multiple business functions Building ...