1 to 25 of 53 Feature Engineering Jobs in the UK

managers Design and deliver machine learning models at scale that drive measurable impact for our business Own the full end to end machine learning delivery lifecycle including data exploration, feature engineering, model selection and tuning, offline and online evaluation, deployments and maintenance Work with MLE Team and Stakeholders to help design and deliver data driven products using Trainline … and platforms like ML Flow Have experience with agile delivery methodologies and CI/CD processes and tools Have a broad of understanding of data extraction, data manipulation and feature engineering techniques Are familiar with statistical methodologies. Have good communication skills Nice to have Understanding and/or hands-on experience of Reinforcement Learning theories, frameworks, and algorithms More ❯

management, enhancing customer experiences, and ensuring regulatory compliance on a global scale. Key Responsibilities End-to-End Model Development: Lead the entire ML lifecyclefrom problem framing, data exploration, and feature engineering to model training, validation, deployment, and monitoring in production environments. Advanced Algorithm Design: Develop and implement sophisticated machine learning algorithms (e.g., Gradient Boosting, NLP, Deep Learning, Graph More ❯

to-have. ML and AI: Practical experience using ML modeling libraries like Scikit-Learn, Keras, Tensorflow, PyTorch and similar Generative AI: Some hands-on experience with LLMs for prompt engineering or agents is preferred Cloud Expertise: Building, deploying and monitoring models on cloud like Azure, AWS or GCP is preferred. Foundational Knowledge: A strong foundation in statistics, mathematics, AI … principles and programming is essential for success in this role. Ideally, you will also have Problem-Solving: Ability to translate business assumptions and rules into feature engineering and model explainability, addressing business problems with data-driven solutions. Collaborative Development: Work under the guidance of senior data scientists and solution architects to build models that align with strategic visions More ❯

teams to automate workflows, monitor performance, and retrain models as needed. You'll bring a passion for building reliable ML infrastructure, a strong technical foundation in modern machine learning engineering, and a track record of working in environments where reliability and scale are paramount. Our Data & Analytics team has grown significantly over the last 18 months, with strong ongoing … ML pipelines for training, deployment, monitoring, and retraining at scale. Working with data scientists to take models from development to production-grade systems, ensuring scalability, reproducibility, and robustness. Automating feature engineering and data pipeline processes, ensuring reproducibility and auditability. Implementing monitoring and observability to detect drift, bias, and performance degradation, and setting up rollback/recovery processes. Using … Pipelines, Kubeflow, Weights & Biases) for experiment tracking, model registry, and automated deployment. Leveraging Docker/Kubernetes and workflow orchestration tools (Airflow, Prefect, Dagster). Collaborating with product, design, and engineering teams to deliver ML features that directly impact customer experience. Translating model performance into business metrics (e.g., accuracy vs cost/latency trade-offs). Monitoring deployed solutions in More ❯

teams to automate workflows, monitor performance, and retrain models as needed. You'll bring a passion for building reliable ML infrastructure, a strong technical foundation in modern machine learning engineering, and a track record of working in environments where reliability and scale are paramount. Our Data & Analytics team has grown significantly over the last 18 months, with strong ongoing … ML pipelines for training, deployment, monitoring, and retraining at scale. Working with data scientists to take models from development to production-grade systems, ensuring scalability, reproducibility, and robustness. Automating feature engineering and data pipeline processes, ensuring reproducibility and auditability. Implementing monitoring and observability to detect drift, bias, and performance degradation, and setting up rollback/recovery processes. Using … Pipelines, Kubeflow, Weights & Biases) for experiment tracking, model registry, and automated deployment. Leveraging Docker/Kubernetes and workflow orchestration tools (Airflow, Prefect, Dagster). Collaborating with product, design, and engineering teams to deliver ML features that directly impact customer experience. Translating model performance into business metrics (e.g., accuracy vs cost/latency trade-offs). Monitoring deployed solutions in More ❯

teams to automate workflows, monitor performance, and retrain models as needed. You'll bring a passion for building reliable ML infrastructure, a strong technical foundation in modern machine learning engineering, and a track record of working in environments where reliability and scale are paramount. Our Data & Analytics team has grown significantly over the last 18 months, with strong ongoing … ML pipelines for training, deployment, monitoring, and retraining at scale. Working with data scientists to take models from development to production-grade systems, ensuring scalability, reproducibility, and robustness. Automating feature engineering and data pipeline processes, ensuring reproducibility and auditability. Implementing monitoring and observability to detect drift, bias, and performance degradation, and setting up rollback/recovery processes. Using … Pipelines, Kubeflow, Weights & Biases) for experiment tracking, model registry, and automated deployment. Leveraging Docker/Kubernetes and workflow orchestration tools (Airflow, Prefect, Dagster). Collaborating with product, design, and engineering teams to deliver ML features that directly impact customer experience. Translating model performance into business metrics (e.g., accuracy vs cost/latency trade-offs). Monitoring deployed solutions in More ❯

engineering. Design and implement AI-powered solutions, leveraging Python, SQL/NoSQL, and APIs for seamless integration. Prototype and rigorously test AI applications, focusing on improving performance through strategic feature engineering and model optimization. Engage with the latest open-source AI/ML tools to enhance business solutions continuously. Develop, deploy, and manage machine learning models aimed at … of various ML algorithms including supervised, unsupervised, and deep learning. Strong problem-solving and effective communication skills. Qualifications: Bachelors or Masters degree in Computer Science, Data Science, Machine Learning, Engineering, or related field. Benefits: 25 days annual leave plus bank holidays Performance-based bonus scheme linked to individual, team, and company achievements Generous Defined Contribution Pension scheme Permanent Health More ❯

their career by working on real AI projects, learning from experts, and gaining hands-on experience with modern AI tools, models, and data pipelines. Whether you studied Computer Science, Engineering, Maths, Data Science, or a related field, if you have a passion for AI and a hunger to learn — we want to hear from you. What You’ll Be … models using tools like Python, TensorFlow, PyTorch, or Scikit-learn. Explore & Prepare Data : Help clean, transform, and analyse large datasets for AI applications. Experiment & Research : Test new algorithms, perform feature engineering, and help improve model accuracy and efficiency. Deploy & Monitor Models : Learn how to deploy models into production environments and monitor their performance over time. Collaborate Across Teams … expecting a seasoned expert — we’re looking for potential , passion , and a strong foundation . Essential: A recent graduate (or soon-to-be) in Computer Science, AI, Data Science, Engineering, Mathematics, Physics, or a similar discipline A strong interest in AI/ML, data science, or applied statistics Solid programming skills in Python (or similar languages) Familiarity with data More ❯

their career by working on real AI projects, learning from experts, and gaining hands-on experience with modern AI tools, models, and data pipelines. Whether you studied Computer Science, Engineering, Maths, Data Science, or a related field, if you have a passion for AI and a hunger to learn — we want to hear from you. What You’ll Be … models using tools like Python, TensorFlow, PyTorch, or Scikit-learn. Explore & Prepare Data : Help clean, transform, and analyse large datasets for AI applications. Experiment & Research : Test new algorithms, perform feature engineering, and help improve model accuracy and efficiency. Deploy & Monitor Models : Learn how to deploy models into production environments and monitor their performance over time. Collaborate Across Teams … expecting a seasoned expert — we’re looking for potential , passion , and a strong foundation . Essential: A recent graduate (or soon-to-be) in Computer Science, AI, Data Science, Engineering, Mathematics, Physics, or a similar discipline A strong interest in AI/ML, data science, or applied statistics Solid programming skills in Python (or similar languages) Familiarity with data More ❯

their career by working on real AI projects, learning from experts, and gaining hands-on experience with modern AI tools, models, and data pipelines. Whether you studied Computer Science, Engineering, Maths, Data Science, or a related field, if you have a passion for AI and a hunger to learn — we want to hear from you. What You’ll Be … models using tools like Python, TensorFlow, PyTorch, or Scikit-learn. Explore & Prepare Data : Help clean, transform, and analyse large datasets for AI applications. Experiment & Research : Test new algorithms, perform feature engineering, and help improve model accuracy and efficiency. Deploy & Monitor Models : Learn how to deploy models into production environments and monitor their performance over time. Collaborate Across Teams … expecting a seasoned expert — we’re looking for potential , passion , and a strong foundation . Essential: A recent graduate (or soon-to-be) in Computer Science, AI, Data Science, Engineering, Mathematics, Physics, or a similar discipline A strong interest in AI/ML, data science, or applied statistics Solid programming skills in Python (or similar languages) Familiarity with data More ❯

their career by working on real AI projects, learning from experts, and gaining hands-on experience with modern AI tools, models, and data pipelines. Whether you studied Computer Science, Engineering, Maths, Data Science, or a related field, if you have a passion for AI and a hunger to learn — we want to hear from you. What You’ll Be … models using tools like Python, TensorFlow, PyTorch, or Scikit-learn. Explore & Prepare Data : Help clean, transform, and analyse large datasets for AI applications. Experiment & Research : Test new algorithms, perform feature engineering, and help improve model accuracy and efficiency. Deploy & Monitor Models : Learn how to deploy models into production environments and monitor their performance over time. Collaborate Across Teams … expecting a seasoned expert — we’re looking for potential , passion , and a strong foundation . Essential: A recent graduate (or soon-to-be) in Computer Science, AI, Data Science, Engineering, Mathematics, Physics, or a similar discipline A strong interest in AI/ML, data science, or applied statistics Solid programming skills in Python (or similar languages) Familiarity with data More ❯

in Python and familiar with key libraries such as TensorFlow, PyTorch, and scikit-learn.* Experience working with cloud platforms such as Azure, AWS, or GCP, ideally within a data engineering or MLOps context.* Strong understanding of data processing, feature engineering, and model evaluation techniques, with experience handling complex scientific datasets.* Excellent problem-solving and analytical skills, with More ❯

quantitative models and metrics, extend, and maintain quantitative models, metrics and investment frameworks, with rigorous back‐testing, scenario design, and attribution . Integrate new indicators and alternative datasets; formalise feature engineering and signal decay/robustness analysis; implement model risk controls , documentation, and reproducibility. Scale and commercialise proprietary metrics for investment use‐cases and new revenue lines (e.g. … in Physics/Mathematics/Computer Science/Quantitative Finance or related STEM field (exceptional MSc considered with relevant work experience). 4–7 years in quantitative research/engineering within capital markets; proven delivery of research to production quant solutions at scale. Demonstrable experience with large scale datasets, data engineering workflows, and production automation. Python (production grade … SQL, Linux; strong software engineering (testing, type hints, code review, packaging). Cloud (AWS/Azure/GCP), CI/CD, Docker/Kubernetes; workflow orchestration (Airflow/Prefect); experiment tracking (MLflow/W&B). Machine Learning for time series/tabular data; LLM/agentic systems, retrieval pipelines, and evaluation/guardrails. Research driven, rigorous and curious More ❯

lifecycle of AI solution development from concept to deployment and maintenance. Utilize programming skills in Python to automate processes and enhance functionality. Work with large datasets for data preprocessing, feature engineering, and model evaluation. Develop and maintain REST APIs and microservices architectures. Monitor, evaluate, and apply governance frameworks for AI/ML models in production environments. Prepare technical … documentation and effectively communicate complex concepts to both technical and non-technical stakeholders. Required Skills & Qualifications: Minimum 3 years of experience in AI solution engineering, specifically in enterprise environments. Demonstrable experience with Microsoft Azure AI services, including AI Foundry and Microsoft Copilot Studio. Proficiency in Python and familiarity with machine learning frameworks such as TensorFlow, PyTorch, and scikit-learn. … Experience with Large Language Models, prompt engineering, and RAG implementations. Strong skills in data analytics, API development, and MLOps practices including CI/CD for ML. Excellent technical documentation and communication skills. Desirable knowledge in Docker, Kubernetes, and understanding of financial services or regulatory environments. In the first instance, please submit your CV. More ❯

grade AI systems that serve millions You'll collaborate across functions and build scalable ML tools from the ground up You'll grow quickly in a lean, high-impact engineering environment What You'll Do Work with product, data, and engineering teams to define ML goals and technical strategies Design, build, and deploy machine learning models that power … personalization, automation, and insight generation Manage the ML lifecycle: data preprocessing, feature engineering, model training, evaluation, and deployment Build scalable ML infrastructure and deployment pipelines Integrate ML outputs into user-facing products and backend systems Stay current with AI/ML trends and apply relevant innovations into our stack Contribute to debugging, testing, and optimization of production ML … don't throw you off Learn obsessively and share openly - you level up fast and help others do the same Requirements Bachelor's degree in Computer Science, Data Science, Engineering, or a related technical field Strong proficiency in Python is a must 2 - 4 years of experience in machine learning or backend software development Experience using frameworks like TensorFlow More ❯

Key Responsibilities Develop, train, and optimize machine learning models for production use. Collaborate with data scientists to turn research prototypes into production-grade solutions. Build robust data pipelines and feature engineering workflows. Deploy ML solutions into cloud environments (AWS, GCP, or Azure). Implement monitoring, testing, and model performance evaluation frameworks. Work with engineering teams to ensure … and frameworks such as PyTorch, TensorFlow, or Scikit-learn). Proven experience in developing and deploying machine learning models in production. Solid understanding of data structures, algorithms, and software engineering principles. Experience with ML pipelines and orchestration tools (e.g., Airflow, Kubeflow, MLflow). Proficiency in working with cloud services (AWS, GCP, or Azure). Strong understanding of CI/… NLP, computer vision, or time-series forecasting. Familiarity with distributed computing frameworks (Spark, Ray). Experience with MLOps and model governance practices. Previous contract experience in a similar ML engineering role. Contract Details Duration: 6–12 months (extension possible) Location: London (Hybrid working model) Day Rate: Competitive, depending on experience More ❯

Key Responsibilities Develop, train, and optimize machine learning models for production use. Collaborate with data scientists to turn research prototypes into production-grade solutions. Build robust data pipelines and feature engineering workflows. Deploy ML solutions into cloud environments (AWS, GCP, or Azure). Implement monitoring, testing, and model performance evaluation frameworks. Work with engineering teams to ensure … and frameworks such as PyTorch, TensorFlow, or Scikit-learn). Proven experience in developing and deploying machine learning models in production. Solid understanding of data structures, algorithms, and software engineering principles. Experience with ML pipelines and orchestration tools (e.g., Airflow, Kubeflow, MLflow). Proficiency in working with cloud services (AWS, GCP, or Azure). Strong understanding of CI/… NLP, computer vision, or time-series forecasting. Familiarity with distributed computing frameworks (Spark, Ray). Experience with MLOps and model governance practices. Previous contract experience in a similar ML engineering role. Contract Details Duration: 6–12 months (extension possible) Location: London (Hybrid working model) Day Rate: Competitive, depending on experience More ❯

Key Responsibilities Develop, train, and optimize machine learning models for production use. Collaborate with data scientists to turn research prototypes into production-grade solutions. Build robust data pipelines and feature engineering workflows. Deploy ML solutions into cloud environments (AWS, GCP, or Azure). Implement monitoring, testing, and model performance evaluation frameworks. Work with engineering teams to ensure … and frameworks such as PyTorch, TensorFlow, or Scikit-learn). Proven experience in developing and deploying machine learning models in production. Solid understanding of data structures, algorithms, and software engineering principles. Experience with ML pipelines and orchestration tools (e.g., Airflow, Kubeflow, MLflow). Proficiency in working with cloud services (AWS, GCP, or Azure). Strong understanding of CI/… NLP, computer vision, or time-series forecasting. Familiarity with distributed computing frameworks (Spark, Ray). Experience with MLOps and model governance practices. Previous contract experience in a similar ML engineering role. Contract Details Duration: 6–12 months (extension possible) Location: London (Hybrid working model) Day Rate: Competitive, depending on experience More ❯

Key Responsibilities Develop, train, and optimize machine learning models for production use. Collaborate with data scientists to turn research prototypes into production-grade solutions. Build robust data pipelines and feature engineering workflows. Deploy ML solutions into cloud environments (AWS, GCP, or Azure). Implement monitoring, testing, and model performance evaluation frameworks. Work with engineering teams to ensure … and frameworks such as PyTorch, TensorFlow, or Scikit-learn). Proven experience in developing and deploying machine learning models in production. Solid understanding of data structures, algorithms, and software engineering principles. Experience with ML pipelines and orchestration tools (e.g., Airflow, Kubeflow, MLflow). Proficiency in working with cloud services (AWS, GCP, or Azure). Strong understanding of CI/… NLP, computer vision, or time-series forecasting. Familiarity with distributed computing frameworks (Spark, Ray). Experience with MLOps and model governance practices. Previous contract experience in a similar ML engineering role. Contract Details Duration: 6–12 months (extension possible) Location: London (Hybrid working model) Day Rate: Competitive, depending on experience More ❯

Key Responsibilities Develop, train, and optimize machine learning models for production use. Collaborate with data scientists to turn research prototypes into production-grade solutions. Build robust data pipelines and feature engineering workflows. Deploy ML solutions into cloud environments (AWS, GCP, or Azure). Implement monitoring, testing, and model performance evaluation frameworks. Work with engineering teams to ensure … and frameworks such as PyTorch, TensorFlow, or Scikit-learn). Proven experience in developing and deploying machine learning models in production. Solid understanding of data structures, algorithms, and software engineering principles. Experience with ML pipelines and orchestration tools (e.g., Airflow, Kubeflow, MLflow). Proficiency in working with cloud services (AWS, GCP, or Azure). Strong understanding of CI/… NLP, computer vision, or time-series forecasting. Familiarity with distributed computing frameworks (Spark, Ray). Experience with MLOps and model governance practices. Previous contract experience in a similar ML engineering role. Contract Details Duration: 6–12 months (extension possible) Location: London (Hybrid working model) Day Rate: Competitive, depending on experience More ❯

Machine Learning Engineer Are you excited by the idea of building intelligent systems that learn from data? Are you looking to start a career at the intersection of software engineering and data science? We are looking for a motivated and curious Graduate Machine Learning Engineer to join our growing AI & Machine Learning team. In this role, you’ll help … machine learning models that solve real-world problems — all while receiving hands-on training, mentoring, and support from experienced ML engineers and data scientists. Whether you studied Computer Science, Engineering, Data Science, or Maths — if you're passionate about machine learning, love to build, and eager to learn, this is the perfect opportunity to kickstart your career. What You … learning models using Python and ML frameworks like TensorFlow, PyTorch, or Scikit-learn Data Preparation : Work on collecting, cleaning, transforming, and analysing large datasets for training and testing models Feature Engineering : Support the design and selection of relevant input features to improve model performance Model Evaluation : Learn to test models for accuracy, robustness, and fairness using best practices More ❯

Machine Learning Engineer Are you excited by the idea of building intelligent systems that learn from data? Are you looking to start a career at the intersection of software engineering and data science? We are looking for a motivated and curious Graduate Machine Learning Engineer to join our growing AI & Machine Learning team. In this role, you’ll help … machine learning models that solve real-world problems — all while receiving hands-on training, mentoring, and support from experienced ML engineers and data scientists. Whether you studied Computer Science, Engineering, Data Science, or Maths — if you're passionate about machine learning, love to build, and eager to learn, this is the perfect opportunity to kickstart your career. What You … learning models using Python and ML frameworks like TensorFlow, PyTorch, or Scikit-learn Data Preparation : Work on collecting, cleaning, transforming, and analysing large datasets for training and testing models Feature Engineering : Support the design and selection of relevant input features to improve model performance Model Evaluation : Learn to test models for accuracy, robustness, and fairness using best practices More ❯

Machine Learning Engineer Are you excited by the idea of building intelligent systems that learn from data? Are you looking to start a career at the intersection of software engineering and data science? We are looking for a motivated and curious Graduate Machine Learning Engineer to join our growing AI & Machine Learning team. In this role, you’ll help … machine learning models that solve real-world problems — all while receiving hands-on training, mentoring, and support from experienced ML engineers and data scientists. Whether you studied Computer Science, Engineering, Data Science, or Maths — if you're passionate about machine learning, love to build, and eager to learn, this is the perfect opportunity to kickstart your career. What You … learning models using Python and ML frameworks like TensorFlow, PyTorch, or Scikit-learn Data Preparation : Work on collecting, cleaning, transforming, and analysing large datasets for training and testing models Feature Engineering : Support the design and selection of relevant input features to improve model performance Model Evaluation : Learn to test models for accuracy, robustness, and fairness using best practices More ❯

AI Engineer/Machine Learning Specialist with Data Engineering Experience Outside IR35 and Fully Remote - £300 per day - 6 Month Contract Intially Candidates can be base anywhere in the UK We are seeking a technically strong AI Engineer or Machine Learning Specialist to join a dynamic team working on cutting-edge Gen AI and data science initiatives. The ideal … will have hands-on experience in developing and deploying machine learning models, working with large datasets, and applying AI techniques to solve real-world problems. A background in data engineering is highly advantageous, enabling the candidate to contribute across multiple areas of the project lifecycle. Key Responsibilities Design, build, and deploy machine learning models and Gen AI solutions to … maintain data pipelines and infrastructure to support model training, evaluation, and deployment. Apply best practices in model performance tuning, validation, and monitoring. Contribute to cross-functional projects, leveraging data engineering skills to support data ingestion, transformation, and integration. Stay current with advancements in AI, ML, and Gen AI technologies, and evaluate their applicability to ongoing initiatives. Ideal Candidate Strong More ❯

across the organization. Responsibilities Understand business problems and conduct statistical analysis independently. Break down hard problems. Communicate effectively to senior stakeholders. Deliver value end-to-end. Self-serve data engineering and infrastructure as required. Make recommendations on best practice in terms of analysis, machine learning and data science. Have a transformative presence in the team. Develop and implement machine … learning models, including feature engineering, model design, training, and deployment. Perform data mining, exploration, and statistical analysis to uncover trends and actionable insights. Create data visualizations, reports, dashboards, and perform data audits. Leverage predictive models to optimize customer experiences and drive business outcomes. Create automated anomaly detection systems to monitor and ensure data quality and operational performance. Qualifications … Computer Science, or a related field. Doctorate in a quantitative field such as Statistics, Computer Science, Mathematics, or Engineering. A "full stack" data scientist - with extensive expertise of data engineering, analysis and analytics, as well as machine learning. 6+ years of experience working in Data Science, preferably within a Software organization. Experience with financial fraud detection and prevention is More ❯