1 to 25 of 30 Permanent Feature Engineering Jobs in England

Work with large datasets to build predictive and analytical models • Collaborate with data scientists to transition models from research to scalable production systems ML Engineering and Deployment • Design and maintain machine learning pipelines and model deployment infrastructure • Implement automated model training, validation, and monitoring processes • Ensure models are scalable … reliable, and performant in production environments Data Engineering and Infrastructure • Work with data engineers to build efficient data pipelines and feature engineering processes • Optimise data workflows to support model development and experimentation • Contribute to the development of robust ML infrastructure and tooling Collaboration and Product Integration • Work ...

univariate time series. This is structured, high-dimensional, real-world forecasting at a level that actually moves enterprise decisions. Own the full modelling lifecycle. Feature engineering, model selection, validation strategy, uncertainty quantification, production deployment. You'll make the calls and live with the results - which means … goal is for your models to inform what Monq recommends inside live procurement negotiations. Getting there requires working closely with product and engineering - translating probabilistic outputs into something a procurement professional can act on in the moment, not just admire in a dashboard. Bridge research and engineering ...

predictive models embedded in automated workflows, your work will directly influence business operations at scale. You’ll operate at the intersection of modelling, engineering, and intelligent automation. 🌐 What You’ll Do 🧠 Model Development & AI Systems Design Design and train predictive models using AWS SageMaker Develop LLM-powered systems … Bedrock (including Claude integration) Build RAG pipelines combining structured and unstructured data Develop evaluation frameworks for accuracy, bias, and robustness Apply best practices in feature engineering and experimentation 🤖 AI Agent & Workflow Integration Architect reasoning agents using advanced foundation models Use code-generation tooling for automation logic and integration ...

pure management role. They are looking for a strong Data Scientist first, someone comfortable across the full lifecycle from ingestion and feature engineering through to modelling and deployment. Their data estate has evolved over time and is currently spread across multiple siloed systems with differing structures and standards. … systems, and proactively define a clear data and AI roadmap. You will lead a Data team of 5-6 across Data Science and Data Engineering, raising standards while still contributing directly to predictive models and AI driven tools. This is a genuine opportunity to bring structure, clarity and technical ...

integrate LLM-powered solutions for tracing, evaluation, and production monitoring Implement robust experiment tracking, model versioning, and reproducibility practices with full audit trails Design feature engineering approaches and contribute to feature store development Support production models through monitoring, performance analysis, and continuous improvement Apply responsible AI practices … quality controls Experience with experiment tracking and MLOps tooling (MLflow, Weights & Biases, Data Version Control) Experience developing LLM/GenAI applications, including prompt engineering and RAG architectures It Would Be Great If You Also Had Experience In Some Of These, But If Not Well Help You With Them Experience ...

Engineer who will report to the Director of AI Engineering. This role will be a subject matter expert for applied AI within our Engineering organisation. This role suits a hands‐on team lead who builds and ships customer‐facing, production‐grade AI, solving complex problems, shaping long‐term strategy …/CD; fluent with AI coding assistants (Cursor, Copilot, Claude Code) and accountable for outcomes Applied LLMs: Claude or similar; RAG, agents, prompt engineering; embeddings + vector DBs; hybrid search; evaluation/benchmarking Data engineering: ETL; SQL/NoSQL design; data sourcing/ingestion with governance and quality ...

great opportunity. The Role Youll work directly with senior leadership to design, develop, and deliver AI solutions across the business. This includes hands on engineering, stakeholder engagement, and contributing to responsible AI practices. There is also a clear development pathway into a future Lead AI position. What Youll … Doing Designing and delivering AI powered solutions, including generative AI models Engineering and preparing data for AI model development Integrating AI models into existing tech and cloud infrastructure Ensuring compliance with data protection and sector regulations Translating complex AI concepts into clear language for non technical teams Identifying innovation ...

great opportunity. The Role Youll work directly with senior leadership to design, develop, and deliver AI solutions across the business. This includes hands on engineering, stakeholder engagement, and contributing to responsible AI practices. There is also a clear development pathway into a future Lead AI position. What Youll … Doing Designing and delivering AI powered solutions, including generative AI models Engineering and preparing data for AI model development Integrating AI models into existing tech and cloud infrastructure Ensuring compliance with data protection and sector regulations Translating complex AI concepts into clear language for non technical teams Identifying innovation ...

Bring: We are seeking a seasoned professional who is excited by the unique challenges of AI data. Must-Have Skills: Extensive Data Engineering Experience: Proven track record (3+ years) in designing, building, and maintaining large-scale data pipelines and data warehousing solutions. Cloud Platform Mastery: Expert-level proficiency with … Kafka, and data orchestration tools such as Apache Airflow or Prefect. ML Data Acumen: Solid understanding of data requirements for machine learning models, including feature engineering, data validation, and dataset versioning. Vector Database Experience: Practical experience working with vector databases (e.g., Pinecone, Milvus, Chroma) for embedding storage ...

passionate about architecting modern data platforms that power real AI at scale — this opportunity is for you. This is more than a data engineering role! You will define and scale the organisation’s data platform to power advanced analytics, machine learning, and AI-driven products. Combining deep technical expertise … time pipelines Champion data modelling standards, semantic layers, and metric governance 🤖 AI & ML Enablement at Scale Architect data foundations supporting the ML lifecycle Design feature stores, embedding pipelines, and AI-ready datasets Enable MLOps workflows (data versioning, monitoring, retraining triggers) Support production inference (batch and real-time) Evaluate ...

tangible mark on how people develop their careers. This is a role for someone who's done real AI work inside a high-calibre engineering organisation and worked closely with product. What you'll do Design and deploy LLM/RAG systems (GPT, Claude, Gemini) powering content generation, semantic … model in a notebook you've taken it to real users, measured it, and iterated. You've done this inside organisations that take engineering seriously. Whether that's a top-tier tech company, a defence/intelligence-grade AI startup, or a fast-scaling data-led business you know ...

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 ...

conversational analytics products • Develop LLM-driven systems including fine-tuning and prompt pipelines • Apply statistical modelling and quantitative analysis to financial datasets • Conduct feature engineering, model evaluation, and experimentation • Work with engineering teams to integrate models into AWS-based platforms • Support development of AI systems used ...

cloud and on-prem data platforms , automation, and emerging AI-driven capabilities, and is specifically suited to candidates with strong, hands-on data engineering experience , particularly in building, operating, and optimising complex data pipelines, data models, and integration workflows at scale. Role Overview and Responsibilities As a Data & Automation … delivery processes using technologies such as Azure Data Factory, Databricks, SSIS, and SQL . Reducing manual interventions through automation and standardisation , including data preparation, feature engineering, and training-data pipelines. Preparing data models and datasets (DWH/Lakehouse) to support business intelligence, analytics, and operational reporting. Monitoring ...

Senior Data Scientist to take a leading role across a portfolio of technically demanding projects. You’ll operate end-to-end - shaping problem definitions, engineering data pipelines, developing advanced models, and ensuring outputs are robust, explainable and deployable in real-world settings. This is a hands-on role … impact. Key Responsibilities Translating complex, ambiguous problem statements into clear, actionable data science solutions. Owning the full data science lifecycle, from data ingestion and feature engineering through to modelling, evaluation and deployment. Developing statistical, machine learning and deep learning models to support high-impact, real-world decision making. ...

Develop and support analytical and statistical models using Python 🛠 Work with real-world energy and environmental datasets, including data cleaning, validation, and feature engineering ☁️ Use AWS services to support data storage, processing, and analytics workflows 🗄 Query and manipulate structured datasets using SQL 📊 Build reports, dashboards, and visualisations that … tailored to experience, including bonus, pension, and generous annual leave ✅ What we’re looking for: 🎓 A degree in Data Science, Computer Science, Mathematics, Statistics, Engineering, or a related field 💼 0–2 years of experience in data analysis or data science (graduate roles, internships, placements, or academic projects welcome) 🐍 Strong ...

solutions that move into production, and wants to learn from experienced engineers in a fast-moving environment. You’ll be part of a modern engineering function working on large-scale data and ML products, contributing to model development, data pipelines, and deployment workflows that power business-critical platforms. … Build, train, and evaluate machine learning models using Python 📊 Work with large, complex datasets to support ML-driven products and insights 🧠 Assist with feature engineering, experimentation, and model optimisation ☁️ Contribute to data and ML pipelines in cloud-based environments 🗄 Work with structured and semi-structured data using ...

Join our dynamic team and be part of building a greener, cleaner future! About the Role : The Technical Delivery Lead is a hands-on engineering role responsible for designing, building, and optimising solutions across Microsoft Fabric, Azure Integration Services (Logic Apps & Function Apps), Power Platform, and Power BI. This … role brings together technical engineering and strategic influence, contributing to technology direction, security standards, and architectural best practices in alignment with the Head of IT. The ideal candidate is a highly capable technical lead with strong data engineering and security awareness, who enjoys solving complex problems and delivering ...

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 ...

things that go into production, and wants to learn from experienced engineers in a fast-moving environment. You’ll be part of a modern engineering function working on large-scale data and AI products, contributing to model development, data pipelines, and deployment workflows that power mission-critical platforms. … Build, train, and evaluate machine learning models using Python 📊 Work with large, complex datasets to support AI-driven products and insights 🧠 Assist with feature engineering, model experimentation, and performance optimisation ☁️ Contribute to data and ML pipelines in cloud-based environments 🗄 Work with structured and semi-structured data using ...

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 ...

ownership and production-grade model deployment. The Role As Senior Data Scientist, you will: Own end-to-end ML solutions -from problem framing and feature engineering to deployment, monitoring, and governance Translate business objectives into modelling strategies aligned to risk appetite and operational constraints Build and deploy models … using Python, SQL, and AWS (SageMaker or equivalent) Partner closely with Engineering, Data, and Risk/Financial Crime teams to ensure robust, production-ready solutions Establish monitoring frameworks for performance, drift, and retraining Drive clear documentation, traceability, and governance appropriate for a regulated environment This role requires someone ...

will: Lead and develop a growing Data Science team, setting standards and delivery cadence Own end-to-end ML solutions -from problem framing and feature engineering to deployment, monitoring, and governance Translate business objectives into modelling strategies aligned to risk appetite and operational constraints Build and deploy models … using Python, SQL, and AWS (SageMaker or equivalent) Partner closely with Engineering, Data, and Risk/Financial Crime teams to ensure robust, production-ready solutions Establish monitoring frameworks for performance, drift, and retraining Drive clear documentation, traceability, and governance appropriate for a regulated environment This role requires someone ...

across the organisation. Key Actions Contribute to Advanced AI and Machine Learning Development: Support delivery of the machine learning lifecycle, including data ingestion, exploration, feature engineering, modelling, evaluation, and assisting with deployment and monitoring Help Drive Innovation and Shape AI Opportunities: Identify where analytics or machine learning … projects, delivering high-quality outputs that support commercial or customer outcomes Engage Stakeholders and Support AI Adoption: Work with teams across actuarial, operations, product, engineering, commercial, and clinical areas to understand needs and help shape solutions Contribute to Developing the Data Science Community: Participate in knowledge sharing sessions, workshops ...

across the organisation. Key Actions Contribute to Advanced AI and Machine Learning Development: Support delivery of the machine learning lifecycle, including data ingestion, exploration, feature engineering, modelling, evaluation, and assisting with deployment and monitoring Help Drive Innovation and Shape AI Opportunities: Identify where analytics or machine learning … projects, delivering high-quality outputs that support commercial or customer outcomes Engage Stakeholders and Support AI Adoption: Work with teams across actuarial, operations, product, engineering, commercial, and clinical areas to understand needs and help shape solutions Contribute to Developing the Data Science Community: Participate in knowledge sharing sessions, workshops ...