5 of 5 Remote/Hybrid Feature Engineering Jobs in London

realise the full potential of an AI enabled future. Key Responsibilities Build and ship production ready AI/ML features-from data ingestion and feature engineering to model training, evaluation, and deployment Develop LLM/GenAI solutions (prompt engineering, tool use, guardrails) and RAG pipelines (chunking, embeddings … efficient memory/latency tuning Build and maintain MLOps/LLMOps workflows-CI/CD for models and prompts, model registry/versioning, feature stores, and automated promotion across environments Instrument observability for data, models, and prompts (telemetry, metrics, traces, dashboards, alerts); implement A/B tests and online ...

complex digital transformation programmes. With access to modern data platforms, high quality datasets, and advanced statistical and AI frameworks, you will work closely with engineering, product, and analytics teams to build solutions that are accurate, explainable, and scalable. This role empowers you to shape end to end analytical ecosystems … data engineers, analysts, product managers, and domain experts to translate business requirements into well defined modelling tasks Build end to end ML pipelines-from feature engineering and preprocessing to deployment ready model outputs Apply advanced techniques such as NLP, time series forecasting, anomaly detection, optimisation, or LLM/ ...

making across complex digital transformation programmes. With access to cutting edge AI frameworks, high performance computing environments, and modern data platforms, you will guide engineering and data science teams in building secure, scalable, and ethical AI systems. This role empowers you to shape end to end AI ecosystems-accelerating … more intelligent, AI enabled future. Key Responsibilities Define the enterprise AI architecture vision and reference patterns; align them to business goals, risk posture, and engineering standards across cloud and hybrid environments Design secure, scalable AI solutions covering data ingestion, feature engineering, model training, inference, and continuous feedback ...

manner using reports or presentations. What you'll bring Proven commercial experience delivering data science projects end-to-end-from exploratory data analysis and feature engineering to model development, validation, and translating results into actionable insights. Fluent with data manipulation and ML frameworks for EDA, feature extraction … practices including abstraction, exceptions handling, and testing to support long-term maintainability. Highly skilled in SQL , especially in the context of data exploration, reporting, feature creation/extraction, and integration with analytic workflows. Proficiency in building interactive data visualisations using tools and frameworks such as Dash, Streamlit , or Superset ...

Government (Grade 7 equivalent), or a highly regulated industry . Essential Experience: End-to-End ML: Proven experience delivering applied data science projects from feature engineering to deployment. Python Mastery: Expert-level Python skills with a focus on code structure, testing, and version control. Governance: A deep commitment ...