126 to 150 of 234 Feature Engineering Jobs in England

AI/ML use cases. You'll work with diverse datasets spanning prepaid, financial services, gambling, and payments, supporting business-critical decisions with high-quality, well-structured data. While engineering will be your focus, you'll also need to collaborate across analytics and product functions-comfortable switching between roles to meet team goals. This role includes occasional global travel … the role: Data Pipeline Development: Build and maintain batch and streaming pipelines using Azure Data Factory and Azure Databricks. Data Categorisation & Enrichment: Structure unprocessed datasets through tagging, standardisation, and feature engineering. Automation & Scripting: Use Python to automate ingestion, transformation, and validation processes. ML Readiness: Work closely with data scientists to shape training datasets, applying sound feature selection techniques. … Data Validation & Quality Assurance: Ensure accuracy and consistency across data pipelines with structured QA checks. Collaboration: Partner with analysts, product teams, and engineering stakeholders to deliver usable and trusted data products. Documentation & Stewardship: Document processes clearly and contribute to internal knowledge sharing and data governance. Platform Scaling: Monitor and tune infrastructure for cost-efficiency, performance, and reliability as data More ❯

emerging technologies, industry trends, and best practices , ensuring the data platform remains cutting-edge. Key Skills & Experience 10+ years of experience in data architecture, solution architecture, or cloud data engineering , with a focus on data platform strategy and leadership . Proven experience leading and mentoring teams in data platform architecture and solution design. Deep expertise in Azure data services … MDM, data governance, and data quality tools . Experience designing and integrating Machine Learning (ML) architectures on Azure , including: MLOps and model lifecycle management in Azure ML and Databricks Feature engineering and feature stores in a cloud-based data ecosystem Model deployment, serving, and monitoring within a Lakehouse or Data Mesh framework. Proficiency in Apache Spark, Python More ❯

database networking, maintaining data security, and configuring user access and authentication protocols in a modern data warehouse. Proficiency in Python, git, SQL, AWS and Snowflake. Strong understanding of data engineering best practices, and experience with data pipelines, data structures, data platforms, data integration and data governance. It would be a plus if you also possess previous experience in: Experience … building and managing AWS cloud infrastructure. Experience in feature engineering for data consumption by AI Applications. Join our winning team today. Together, we'll accelerate the real-life impact of tomorrow's science and technology. We partner with customers across the globe to help them solve their most complex challenges, architecting solutions that bring the power of science More ❯

at scale to help optimise product bidding and maximise profit across platforms like Google Shopping. Expect to work across the full ML lifecycle—from exploring large, messy datasets and engineering features, to evaluating models offline and running experiments to validate impact. At the same time, you'll build, deploy and monitor models in production: setting up retraining workflows, pipeline … clean, documented, well tested and reviewed code and have tooling and a culture to support this. This is a hands-on, high-impact role that blends research, experimentation and engineering, all tied to clear business outcomes. You'll collaborate closely with marketers, data analysts and engineers, and play a key part in shaping the future of how we scale … at scale to help optimise product bidding and maximise profit across platforms like Google Shopping. Expect to work across the full ML lifecycle—from exploring large, messy datasets and engineering features, to evaluating models offline and running experiments to validate impact. At the same time, you'll build, deploy and monitor models in production: setting up retraining workflows, pipeline More ❯

funnel from acquisition to retention, providing data-driven recommendations that improve marketing effectiveness, product journeys, and features. Shape Predictive Modeling by collaborating with ML engineers to influence methodology selection, feature engineering, and performance evaluation of models that reveal lifetime value of users, enabling smarter investment decisions. Uncover Hidden Patterns within user behavioural data, campaign touchpoints, and conversion journeys … complex analyses into compelling narratives that influence decision-makers A collaborative mindset that thrives in fast-paced environments where you'll work directly with product, marketing, finance, analytics, and engineering teams How we work We're a mission-led, product-driven team. We move fast, stay focused and take ownership - from brief to build to impact. Debate is encouraged. More ❯

improve machine understanding of our data. Identifying areas where LLMs/ML can be used to extract useful insights to improve client experience. Integrate state-of-the-art prompt engineering methods into our LLM-driven products to improve performance. Write production-ready code to execute ML/AI pipelines efficiently, and build these into production systems Conduct novel research … a recommender system and how they work. Experience working with unstructured natural language data and LLMs. Experience in programming in Python. Strong skills in data analysis, data visualisation, and feature engineering. Natural creativity with a track record of exploring innovative use cases of data and applications of statistical/ML methods. Strong communication skills; explaining complex technical concepts to … status, disability, veteran status, genetic information, sexual orientation and gender identity or expression. Seniority level Seniority level Entry level Employment type Employment type Full-time Job function Job function Engineering and Information Technology Industries Data Infrastructure and Analytics Referrals increase your chances of interviewing at Acin by 2x Get notified about new Data Scientist jobs in London, England, United More ❯

and analytics Clear and confident communication skills (written and verbal) A blend of technical know-how and creativity in model development A curious, investigative mindset to explore and test feature engineering techniques Excellent attention to detail and data quality Ability to work both independently and collaboratively in a team environment Proficiency in R Solid knowledge of SQL Working More ❯

Why join us Joining Sainsbury's Tech means becoming part of an inclusive and driven team that is passionate about creating innovative solutions. As an Engineering Manager, you will play a pivotal role in leading and coaching talented engineering teams, driving the delivery of impactful solutions that drive efficiency and enhance performance across the business. With a focus … on continuous improvement, you will have the opportunity to shape a world-class engineering function and contribute to the development of cutting-edge processes and technologies. At Sainsbury's, we value collaboration, diversity, and inclusivity, and offer a supportive and inspiring environment where you can make a purposeful contribution. Join us to be a part of an organisation that … fosters technical excellence and embraces the power of diversity to shape the technology landscape of a leading UK retailer. About the Team You'll lead a multi-skilled engineering team at Sainsbury’s, where we lead the development and management of our new Artificial Intelligence & Machine Learning platform. What you'll do Own and evolve a newly built ML More ❯

the scale it takes for us to feed the nation. The level of data, transactions and variety it involves. Then you'll realise that ours is a modern software engineering environment because it has to be. We've made serious investment into a Tech Academy and into setting standards and principles. We iterate, learn, experiment and push ways of … in everything from AI to reusable tech. Joining Sainsbury's Tech means becoming part of an inclusive and driven team that is passionate about creating innovative solutions. As an Engineering Manager, you will play a pivotal role in leading and coaching talented engineering teams, driving the delivery of impactful solutions that drive efficiency and enhance performance across the … business. With a focus on continuous improvement, you will have the opportunity to shape a world-class engineering function and contribute to the development of cutting-edge processes and technologies. At Sainsbury's, we value collaboration, diversity, and inclusivity, and offer a supportive and inspiring environment where you can make a purposeful contribution. Join us to be a part More ❯

Machine Learning Engineering Manager (Operations) Join to apply for the Machine Learning Engineering Manager (Operations) role at THG Ingenuity . About THG Ingenuity THG Ingenuity is a fully integrated digital commerce ecosystem, designed to power brands without limits. Our global end-to-end tech platform includes three products: THG Commerce, THG Studios, and THG Fulfilment. Each offers a … goals. Oversee the entire lifecycle of ML projects from conception to deployment and monitoring. Guide the team in building, training, and deploying models. Ensure best practices in data preparation, feature engineering, and model validation. Establish workflows for deployment, monitoring, and scaling of models. Qualifications Proven experience as a Machine Learning Engineer, with leadership experience in deploying models in More ❯

We are seeking a seasoned Principal Engineer to lead the design, development, and evolution of our Observability Platform , ensuring it meets the needs of our rapidly scaling systems and engineering teams. This role will also focus on leveraging Machine Learning (ML) and Artificial Intelligence (AI) to deliver advanced insights that proactively improve system health and drive down Mean Time … the development and adoption of platform capabilities to ensure system health, reliability, and performance. Establish and evolve platform standards and best practices to align with the company's overall engineering goals. Strategic Initiatives Collaborate with engineering teams to define the observability strategy, ensuring alignment with business and operational objectives. Identify and integrate the latest observability technologies, including AI … performant, and secure across all environments. Optimize data collection, processing, and storage to balance performance with cost efficiency. Define SLAs, SLOs, and SLIs for observability services to support reliability engineering practices. Continuously improve MTTD and MTTR by leveraging advanced AI/ML models for predictive analysis and automated responses. Mentorship and Collaboration Act as a mentor and technical leader More ❯

the scale it takes for us to feed the nation. The level of data, transactions and variety it involves. Then you’ll realise that ours is a modern software engineering environment because it has to be. We’ve made serious investment into a Tech Academy and into setting standards and principles. We iterate, learn, experiment and push ways of … in one of our cross-functional squads - helping to design, build, and run scalable ML systems that create real value for customers and colleagues. You will - Work closely with engineering, data science, product, and architecture peers to deliver robust, production-ready machine learning solutions. Provide technical leadership within the team, contribute to shaping best practices, and drive the successful … delivery and sustainability of ML capabilities. What you’ll do: Lead the technical delivery of ML solutions from design through to deployment, including feature engineering, training, testing, serving, and monitoring. Partner with Data Scientists to co-design scalable model pipelines and infrastructure that enable experimentation, rapid iteration, and reliable production deployments. Work as the technical lead for engineering More ❯

risk, fraud detection, pricing, and customer engagement. You will own the full lifecycle of your project, including the discovery of business opportunities through statistical analysis, data curation and processing, feature engineering, development of machine learning models, deployment to production, and model monitoring. You will engage with senior stakeholders across the company, influence critical business decisions, and make direct … Lead high impact projects related to data and modelling. Own the full lifecycle of your project, including the discovery of business opportunities through statistical analysis, data curation and processing, feature engineering, development of machine learning models, deployment to production, and model monitoring. Engage with senior stakeholders across the company, influence critical business decisions, and make direct impacts on … for success and collaborating openly. You have a can-do attitude and strong commitment to getting the job done. You are proficient in SQL and Python, with good software engineering practices. You have in-depth knowledge of machine learning algorithms (logistic regression, random forest, gradient boosting, neural networks, k-means, etc.) and statistics (Monte Carlo, hypothesis testing, confidence intervals More ❯

happy – by continuously training and deploying machine learning models. We aim to make model deployments as easy and error-free as code deployments. Google’s Best Practices for ML Engineering is our bible. Our models are trained to spot multiple types of fraud, using a variety of data sources and techniques in real time. The prediction pipelines are under … it’s down to the Detection team to investigate why. The Detection team is core to Ravelin’s success. They work in a deeply collaborative partnership with the Data Engineering team to design the data architecture and infrastructure that powers our ML systems. The Role We are looking for a Senior Machine Learning Engineer to join our Detection team. … Beyond just consuming data, you will take a leading role in defining how data is modeled, stored, and served for machine learning purposes, directly influencing the architecture of our feature generation pipelines and ensuring data quality throughout the ML lifecycle. You'll take strategic ownership over several aspects of our ML infrastructure and be empowered to introduce and champion More ❯

to manage and provision our cloud infrastructure for machine learning operations. Oversee the transition to a real-time streaming architecture for our machine learning applications, ensuring efficient data ingestion, feature engineering, and model serving in a streaming context. Develop and implement a comprehensive monitoring framework to track model performance, identify potential issues, and ensure optimal model health in … preferred tool) or similar frameworks like Apache Airflow to manage and automate ML workflows. You have experience with real-time data streaming technologies such as Kafka and Confluent and feature stores in such settings. You are skilled in building and maintaining monitoring systems for machine learning models. You have excellent communication and collaboration skills, enabling you to effectively work More ❯

skills – even better if these skills have been applied to threat detection, malware analysis, phishing and/or abuse detection. Experience building production-grade AI pipelines, including data ingestion, feature engineering, validation, model deployment, and monitoring. Experience designing and implementing anomaly detection, classification, clustering, and retrieval across vision and language models, ideally for identifying cyber threats (URLs, domains … fit to problem space, including scenarios where RAG is applicable. Incident response experience, and ability to work with large, noisy, and rapidly evolving threat datasets. Strong background in cloud engineering and containerisation (Docker, Kubernetes) with experience deploying AI services at scale, particularly on AWS via Terraform. The package offered to the Senior/Principal AI Engineer will consist of More ❯

skills – even better if these skills have been applied to threat detection, malware analysis, phishing and/or abuse detection. Experience building production-grade AI pipelines, including data ingestion, feature engineering, validation, model deployment, and monitoring. Experience designing and implementing anomaly detection, classification, clustering, and retrieval across vision and language models, ideally for identifying cyber threats (URLs, domains … fit to problem space, including scenarios where RAG is applicable. Incident response experience, and ability to work with large, noisy, and rapidly evolving threat datasets. Strong background in cloud engineering and containerisation (Docker, Kubernetes) with experience deploying AI services at scale, particularly on AWS via Terraform. The package offered to the Senior/Principal AI Engineer will consist of More ❯

skills – even better if these skills have been applied to threat detection, malware analysis, phishing and/or abuse detection. Experience building production-grade AI pipelines, including data ingestion, feature engineering, validation, model deployment, and monitoring. Experience designing and implementing anomaly detection, classification, clustering, and retrieval across vision and language models, ideally for identifying cyber threats (URLs, domains … fit to problem space, including scenarios where RAG is applicable. Incident response experience, and ability to work with large, noisy, and rapidly evolving threat datasets. Strong background in cloud engineering and containerisation (Docker, Kubernetes) with experience deploying AI services at scale, particularly on AWS via Terraform. The package offered to the Senior/Principal AI Engineer will consist of More ❯

to manage and provision our cloud infrastructure for machine learning operations. Oversee the transition to a real-time streaming architecture for our machine learning applications, ensuring efficient data ingestion, feature engineering, and model serving in a streaming context. Develop and implement a comprehensive monitoring framework to track model performance, identify potential issues, and ensure optimal model health in … preferred tool) or similar frameworks like Apache Airflow to manage and automate ML workflows. You have experience with real-time data streaming technologies such as Kafka and Confluent and feature stores in such settings. You are skilled in building and maintaining monitoring systems for machine learning models. You have excellent communication and collaboration skills, enabling you to effectively work More ❯

skills – even better if these skills have been applied to threat detection, malware analysis, phishing and/or abuse detection. Experience building production-grade AI pipelines, including data ingestion, feature engineering, validation, model deployment, and monitoring. Experience designing and implementing anomaly detection, classification, clustering, and retrieval across vision and language models, ideally for identifying cyber threats (URLs, domains … fit to problem space, including scenarios where RAG is applicable. Incident response experience, and ability to work with large, noisy, and rapidly evolving threat datasets. Strong background in cloud engineering and containerisation (Docker, Kubernetes) with experience deploying AI services at scale, particularly on AWS via Terraform. The package offered to the Senior/Principal AI Engineer will consist of More ❯

to manage and provision our cloud infrastructure for machine learning operations. Oversee the transition to a real-time streaming architecture for our machine learning applications, ensuring efficient data ingestion, feature engineering, and model serving in a streaming context. Develop and implement a comprehensive monitoring framework to track model performance, identify potential issues, and ensure optimal model health in … preferred tool) or similar frameworks like Apache Airflow to manage and automate ML workflows. You have experience with real-time data streaming technologies such as Kafka and Confluent and feature stores in such settings. You are skilled in building and maintaining monitoring systems for machine learning models. You have excellent communication and collaboration skills, enabling you to effectively work More ❯

with a strong analytical background, capable of maintaining technical contributions while managing a team. Your qualifications include: Experience delivering data science initiatives from concept to production, including data preprocessing, feature engineering, and model evaluation. Strong communication skills for explaining complex concepts to business stakeholders. Proficiency in Python or similar tools, strong SQL skills, and experience with data visualization More ❯

frameworks, and processes to improve the speed, efficiency, and reliability of production services. We'd love to hear from you if you... Hold a degree in mathematics, physics, statistics, engineering, or another quantitative field, or bring relevant professional experience. Have experience designing machine learning algorithms, including feature engineering, model selection, and hyperparameter tuning. Are comfortable working with More ❯

clustering, and retrieval across vision and language models, ideally for identifying cyber threats (URLs, domains, phishing, botnets, etc.) Hands-on experience building production -grade AI pipelines, including data ingestion, feature engineering, validation, model deployment, and monitoring . Proficient in a major backend language and related ML/AI libraries (e.g. Tensorflow & PyTorch , etc), with a preference for Go. … to problem space , including scenarios where RAG is applicable. Incident response experience, and ability to work with large, noisy, and rapidly evolving threat datasets . Strong background in cloud engineering and containerisation (Docker, Kubernetes ), with experience deploying AI services at scale, particularly on AWS via Terraform . Bonus points if you have: Experience with P erl . Experience leveraging More ❯

fairer credit system. The Role We're looking for an experienced MLOps Engineer for a 3-month contract to lead the development of our ML deployment, testing, monitoring, and feature engineering pipelines . You'll be responsible for establishing best practices and production-grade systems to support our machine learning workflows from training to deployment and beyond. The … pipeline using AWS , with a strong focus on SageMaker for training, deployment, and hosting. - Integrate and operationalize MLflow for model versioning, experiment tracking, and reproducibility. - Architect and implement a feature store strategy for consistent, discoverable, and reusable features across training and inference environments (e.g., using SageMaker Feature Store , Feast, or custom implementation). - Work closely with data scientists … to formalize feature engineering workflows , ensuring traceability, scalability, and maintainability of features. - Develop unit, integration, and data validation tests for models and features to ensure stability and quality. - Establish model monitoring and alerting frameworks for real-time and batch inference (e.g., model drift detection, performance degradation). - Build CI/CD pipelines for ML workflows (training, evaluation, deployment More ❯