26 to 37 of 37 Feature Engineering Jobs in London

a forward-thinking trading organisation. This is an exciting opportunity to design and implement the infrastructure that powers advanced machine learning workflows in a production trading environment. Key Responsibilities: Feature Store & Data Lake: Build scalable infrastructure for time-series feature storage, retrieval, and versioning optimised for ML workloads. MLOps Pipelines: Design end-to-end workflows for data ingestion … feature engineering, model training, backtesting, and deployment. Data Ingestion Layer: Connect raw data streams into structured, queryable formats (Parquet/Delta Lake). Production Serving: Deploy feature computation and model inference with appropriate latency characteristics. Integration: Collaborate with existing data capture and execution systems to ensure seamless operations. CI/CD Pipeline: Implement and maintain robust continuous … integration and deployment pipelines for ML models. Requirements: Strong experience in building and maintaining MLOps pipelines. Hands-on experience with feature stores, data lakes, and time-series data. Proficiency with modern data formats like Parquet and Delta Lake. Familiarity with production ML model deployment and latency optimisation. Experience integrating ML workflows with existing data and execution systems. Strong understanding More ❯

as; AI Foundry, Open-AI services, and Microsoft Copilot Studio. Apply governance frameworks for AI/ML models. Python knowledge to automate processes Large datasets experience, including data preprocessing, feature engineering, and model evaluation. Agile development of AI solution from concept to deployment to continuous improvement. Create and maintain technical documentation Communicate complex concepts to all stakeholders. At … least 3 years of experience in AI solution engineering. Large Language Models experience including prompt engineering and RAG implementations. Expert data analytics, MLOps practices and API development. Desirable knowledge in Docker and Kubernetes More ❯

is placing data science at the heart of its strategy, investing heavily in both classic ML and next-generation AI capabilities. This is a company that blends modern product engineering with a fast-moving, high-ownership culture. Their environment encourages experimentation, cross-functional collaboration, and the freedom to shape what “great” looks like in machine learning at scale. The … of their real-time modelling infrastructure. This is a hybrid hands-on/leadership role, ideal for someone who thrives at the intersection of applied research, platform integration, and engineering-minded data science. What You’ll Be Doing Build and deploy a wide variety of models spanning classification, regression, propensity scoring, and LLM-based use cases Spearhead the entire … s GenAI efforts. The team have multiple LLM projects running but would love a technical leader in this area Own the end-to-end lifecycle of ML projects, from feature engineering to deployment and monitoring Define best practices for model testing, automation, and continuous improvement within a high-performing team Act as a technical thought leader, partnering with More ❯

is placing data science at the heart of its strategy, investing heavily in both classic ML and next-generation AI capabilities. This is a company that blends modern product engineering with a fast-moving, high-ownership culture. Their environment encourages experimentation, cross-functional collaboration, and the freedom to shape what “great” looks like in machine learning at scale. The … of their real-time modelling infrastructure. This is a hybrid hands-on/leadership role, ideal for someone who thrives at the intersection of applied research, platform integration, and engineering-minded data science. What You’ll Be Doing Build and deploy a wide variety of models spanning classification, regression, propensity scoring, and LLM-based use cases Spearhead the entire … s GenAI efforts. The team have multiple LLM projects running but would love a technical leader in this area Own the end-to-end lifecycle of ML projects, from feature engineering to deployment and monitoring Define best practices for model testing, automation, and continuous improvement within a high-performing team Act as a technical thought leader, partnering with More ❯

the structure and purpose of their outputs and determine the most effective ways to visualise and communicate them. Data Product Development Support the development of robust data pipelines and feature engineering activities in partnership with Data Engineers. Design and develop a unified, user-friendly front-end interface that integrates dashboards, reports, models, and pipeline outputs into a single … or equivalent). Solid understanding of UX principles and accessibility standards (WCAG 2.2). Ability to collaborate with multidisciplinary teams and communicate complex information effectively. Desirable Experience with data engineering concepts such as pipelines, orchestration, or feature engineering. Familiarity with component-driven design systems. Knowledge of performance optimisation techniques for front-end applications. Experience working in Agile or More ❯

and simulation teams to integrate data science models into larger decision engines Collaborate with software engineers to productionise models and embed them in decision-support platforms Conduct data cleaning, feature engineering, and data pre-processing to ensure model quality and robustness Document model design, assumptions, and data sources Present findings, insights, and recommendations to stakeholders, including non-technical More ❯

and simulation teams to integrate data science models into larger decision engines Collaborate with software engineers to productionise models and embed them in decision-support platforms Conduct data cleaning, feature engineering, and data pre-processing to ensure model quality and robustness Document model design, assumptions, and data sources Present findings, insights, and recommendations to stakeholders, including non-technical More ❯

using dbt and Delta Live Tables to ensure consistency across analytics and AI use cases Enable Generative AI and ML workloads by designing pipelines for vector search, RAG, and feature engineering Implement secure access and governance controls including RBAC, SSO, token policies, and pseudonymisation frameworks Support batch and streaming data flows using technologies like Kafka, Airflow, and Terraform More ❯

using dbt and Delta Live Tables to ensure consistency across analytics and AI use cases Enable Generative AI and ML workloads by designing pipelines for vector search, RAG, and feature engineering Implement secure access and governance controls including RBAC, SSO, token policies, and pseudonymisation frameworks Support batch and streaming data flows using technologies like Kafka, Airflow, and Terraform More ❯

an internal evangelist for applied data science and innovation 🧠 What You’ll Bring Must-haves Strong understanding of machine learning techniques — clustering, NLP, deep learning Hands-on experience with feature engineering, model evaluation and data exploration Knowledge of modern AI tooling (RAG, fine-tuning, LLMs, agentic frameworks) Ability to bridge technical and commercial conversations confidently A logical, creative More ❯

stakeholders. The role will consolidate data, dashboards, reports, models, and analytical outputs into a single, user-friendly Fabric-based experience leveraging Power BI, OneLake, and Fabric’s integrated data engineering and analytics capabilities . The BI Analyst will ensure the solution is visually intuitive, performant, accessible, and aligned with user needs across the organisation. Key Responsibilities Partner with Business … Analysts and Product Teams to identify high-impact analytical opportunities and deliver insights using Microsoft Fabric (Power BI, Data Engineering, Data Factory, Lakehouse) . Conduct exploratory data analysis (EDA) within Fabric to identify patterns, anomalies, and meaningful trends, translating findings into clear visual stories for decision-makers. Work alongside Data Engineers to support data ingestion, transformation, and feature engineering using Fabric’s Data Factory, Lakehouse, and pipelines. Design and develop a single-pane-of-glass analytics interface within Microsoft Fabric, integrating dashboards, reports, semantic models, and data assets into a coherent user experience. Implement WCAG 2.2 accessibility standards , ensuring the interface is inclusive and usable for all audiences. Optimise front-end performance across Fabric workspace components More ❯

role requires comfort with ML systems, RAG architectures, natural-language interfaces, and the practical side of shipping AI features that solve real business problems. Qualifications • Proven experience in software engineering, with strong proficiency in Python. • Solid understanding of core ML fundamentals: supervised/unsupervised learning, model evaluation, and feature engineering. • Experience working with LLM frameworks such as LangChain More ❯