1 to 25 of 28 Permanent Feature Engineering Jobs in the UK excluding London

where the NHSBSA can take you. We are people connected to care. Job description Job responsibilities In this role, you are accountable for: AI Engineering and development 1. Designing, developing, and deploying AI solutions from prototype to production, ensuring scalability, security, and maintainability.2. Collaborate on feature engineering … model development, and MLOps. Shares expertise and mentors colleagues and engineers. Excellent understanding of model lifecycle, from experimentation to production. Strong grasp of software engineering principles (SOLID, design patterns), dependency injection, and scalable system design. Strong understanding of NLP, embeddings, and semantic search. Awareness of ethical and privacy implications ...

Data Scientist | Cardiff/Hybrid | Engineering | AI & Predictive Analytics We're supporting a growing engineering business looking to hire a Data Scientist to help develop scalable ML and AI solutions across compliance, risk and contractor analytics platforms. You'll work closely with AI Engineers, Data Engineers and DevOps … learning initiatives that directly support operational performance, compliance and risk management. You'll be involved in the full model lifecycle from data ingestion and feature engineering through to deployment, optimisation and monitoring. Key Responsibilities Develop machine learning models for risk scoring, churn prediction and contractor analytics Build ...

Lead, Applied Scientist and Research Engineer positions—for upcoming roles across applied machine learning, generative AI, MLOps and model productionisation. These are hands-on engineering roles where you'll build, train, ship and operate the models that drive real product behaviour and real business outcomes. You'll work across … lifecycle—from problem framing and data preparation through to model development, evaluation, deployment, monitoring and iteration. The role suits someone who pairs solid software engineering foundations with genuine ML rigour and a healthy scepticism about what models can and can't be trusted to do. Key Responsibilities Partner with ...

Python and SQL skills Experience developing and deploying machine learning models into production environments Excellent understanding of statistical modelling, experimentation, and model evaluation Strong feature engineering and exploratory data analysis capabilities Experience communicating technical findings and insights to non-technical stakeholders A degree qualification (BSc, MSc, PhD etc. … innovation The Senior Data Scientist role will involve: Developing and deploying machine learning models to solve real-world business challenges Performing exploratory data analysis, feature engineering, model development, and evaluation Building forecasting, classification, segmentation, and predictive modelling solutions Designing and executing statistical analyses and experiments to support strategic ...

Overview We are seeking an AI Engineering Lead to sit at the top of our client’s delivery structure. This role bridges strategic leadership and hands‐on delivery, providing day‐to‐day technical direction to a team of consultants and ensuring high‐quality outcomes across multiple client environments. … operationalisation. Act as the senior technical authority for clients, translating complex concepts into clear defined strategies for senior non‐technical stakeholders. Own solution quality: engineering standards, code reviews, model governance, security, and delivery assurance. Provide technical leadership and coaching; build progression frameworks and mentoring for consultants. Drive pre‐sales ...

position responsible for designing, building, and deploying enterprise-grade AI solutions that drive measurable business value across the organisation. Working closely with IT, Data, Engineering, and senior business stakeholders, you will lead the development and implementation of advanced AI solutions, leveraging Large Language Models (LLMs), Retrieval-Augmented Generation … expertise with strategic thinking, taking solutions from concept through to production deployment and adoption. The ideal candidate will have a strong foundation in AI Engineering combined with a robust Data Science background, enabling them to develop, evaluate, and optimise AI solutions that deliver measurable business outcomes. This role would ...

security platform, contributing to innovative features that leverage advanced analytics, behavioural detection, and intelligent threat prevention. Working as part of a highly collaborative engineering team, you will help bridge the gap between cutting-edge machine learning research and production-grade security software. To see what our engineering teams … design and implementation of intelligent threat detection, behavioural analysis, and anomaly detection capabilities. Participate in architecture discussions, code reviews, testing, and continuous improvement of engineering practices. Evaluate emerging AI technologies and contribute to future product innovation. Requirements for the C++ AI/ML Software Engineer Strong proficiency in modern ...

Leadership Own end-to-end architecture across data and AI layers: Data ingestion, processing, modelling, semantic layer, and consumption AI platform integration (model lifecycle, feature engineering, inference pipelines) Design modern Lakehouse + AI architecture leveraging Azure and Databricks Define architecture for scalable, governed, and reusable AI-ready data … lineage, security, and responsible AI principles 3. AI Platforms & AI-led Data Migration Design and implement AI Platforms integrating: Model development environments, MLOps pipelines, feature stores, and model serving Lead AI-driven migration strategies , including: Automated schema discovery, data mapping, and transformation using AI accelerators AI-assisted code conversion ...

Leadership Own end-to-end architecture across data and AI layers: Data ingestion, processing, modelling, semantic layer, and consumption AI platform integration (model lifecycle, feature engineering, inference pipelines) Design modern Lakehouse + AI architecture leveraging Azure and Databricks Define architecture for scalable, governed, and reusable AI-ready data … lineage, security, and responsible AI principles 3. AI Platforms & AI-led Data Migration Design and implement AI Platforms integrating: Model development environments, MLOps pipelines, feature stores, and model serving Lead AI-driven migration strategies , including: Automated schema discovery, data mapping, and transformation using AI accelerators AI-assisted code conversion ...

logistic regression, gradient boosting, random forests, clustering, NLP Proficient in Python and/or R, with strong experience in data quality, model development and feature engineering Strong command of SQL and familiarity with data engineering environments such as Databricks or similar Skilled in data visualisation and storytelling ...

degree in a highly quantitative field such as mathematics, physics or computer science A quantitative PhD Experience working with real-world datasets (data cleaning, feature engineering, exploratory analysis) Exposure to machine learning model development and evaluation Familiarity with cloud platforms or distributed data tools (e.g. Azure, AWS, Spark … Understanding of software engineering practices (e.g. Git, testing) Experience communicating insights through visualisation or presentations Interest in applying data science to customer or decisioning problems Supervisory Responsibilities: This role does not have supervisory duties. Posting date: Thursday 02nd July 2026 [11.59pm] #LI-SE1 Shannon #LI-Hybrid Skills Artificial Intelligence ...

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. ...

topics, including: Fundamentals of ML and AI (including Machine Learning Lifecycle, Machine Learning Mathematical Foundations); Solving Business Problems with Machine Learning; Data Preparation and Feature Engineering; Planning and preparing ML/AI solution; Data Ethics and Responsible AI; Model Engineering and Training; Model Evaluation and Deployment; Monitoring ...

optimization techniques for LLM-based agents Resource-constrained AI model deployment Vector databases and semantic search (e.g., Pinecone, Weaviate, Milvus, Qdrant , and ChromaDB ) Prompt engineering for efficient agent reasoning LLM fine-tuning and instruction optimization Graph databases for network topology and context management Feature engineering for network … deliver end-to-end services and solutions with our deep industry expertise and strong partner ecosystem, leveraging our capabilities across strategy, technology, design, engineering and business operations. The Group reported 2025 global revenues of €22.5 billion. Make it real | www.capgemini.com ...

using Python, TensorFlow, PyTorch Work on supervised, unsupervised, and reinforcement learning problems Clean and process large datasets to extract insights and train algorithms Perform feature engineering to improve model accuracy Design ML pipelines and automate model deployment Collaborate with software engineers to integrate models into applications Use MLOps ...

then scale into production Shape problem definitions with stakeholders in ambiguous environments Bring best practices from adjacent industries into a high-performance setting Data Engineering & Architecture Design/maintain pipelines integrating telemetry/timing, video/image, event operations, and digital/commercial engagement data Develop APIs/services … secure-by-design delivery with cyber teams What we're looking for Essentials 5-10 years' experience in data science/AI/ML engineering Strong Python Production ML deployment experience (end-to-end) Strong SQL + modern data warehouse experience Cloud familiarity + containerisation/orchestration Streaming data ...

expertise to make sense of complex data, working in close collaboration with our business partners. This role will primarily focus on feature engineering and insight generation from new types of data and the development of machine learning models to address critical business challenges in underwriting. We are interested … Develop and maintain the machine learning models which define our competitive advantage in the financial services market. Explore and evaluate data, using advanced feature generation and categorisation techniques, in order to stay at the forefront of innovation. Analyse tabular and non-tabular data, such as text, logs, or time ...

expertise to make sense of complex data, working in close collaboration with our business partners. This role will primarily focus on feature engineering and insight generation from new types of data and the development of machine learning models to address critical business challenges in underwriting. We are interested … Develop and maintain the machine learning models which define our competitive advantage in the financial services market. Explore and evaluate data, using advanced feature generation and categorisation techniques, in order to stay at the forefront of innovation. Analyse tabular and non-tabular data, such as text, logs, or time ...

expertise to make sense of complex data, working in close collaboration with our business partners. This role will primarily focus on feature engineering and insight generation from new types of data and the development of machine learning models to address critical business challenges in underwriting. We are interested … Develop and maintain the machine learning models which define our competitive advantage in the financial services market. Explore and evaluate data, using advanced feature generation and categorisation techniques, in order to stay at the forefront of innovation. Analyse tabular and non-tabular data, such as text, logs, or time ...

expertise to make sense of complex data, working in close collaboration with our business partners. This role will primarily focus on feature engineering and insight generation from new types of data and the development of machine learning models to address critical business challenges in underwriting. We are interested … Develop and maintain the machine learning models which define our competitive advantage in the financial services market. Explore and evaluate data, using advanced feature generation and categorisation techniques, in order to stay at the forefront of innovation. Analyse tabular and non-tabular data, such as text, logs, or time ...

expertise to make sense of complex data, working in close collaboration with our business partners. This role will primarily focus on feature engineering and insight generation from new types of data and the development of machine learning models to address critical business challenges in underwriting. We are interested … Develop and maintain the machine learning models which define our competitive advantage in the financial services market. Explore and evaluate data, using advanced feature generation and categorisation techniques, in order to stay at the forefront of innovation. Analyse tabular and non-tabular data, such as text, logs, or time ...

expertise to make sense of complex data, working in close collaboration with our business partners. This role will primarily focus on feature engineering and insight generation from new types of data and the development of machine learning models to address critical business challenges in underwriting. We are interested … Develop and maintain the machine learning models which define our competitive advantage in the financial services market. Explore and evaluate data, using advanced feature generation and categorisation techniques, in order to stay at the forefront of innovation. Analyse tabular and non-tabular data, such as text, logs, or time ...

expertise to make sense of complex data, working in close collaboration with our business partners. This role will primarily focus on feature engineering and insight generation from new types of data and the development of machine learning models to address critical business challenges in underwriting. We are interested … Develop and maintain the machine learning models which define our competitive advantage in the financial services market. Explore and evaluate data, using advanced feature generation and categorisation techniques, in order to stay at the forefront of innovation. Analyse tabular and non-tabular data, such as text, logs, or time ...

expertise to make sense of complex data, working in close collaboration with our business partners. This role will primarily focus on feature engineering and insight generation from new types of data and the development of machine learning models to address critical business challenges in underwriting. We are interested … Develop and maintain the machine learning models which define our competitive advantage in the financial services market. Explore and evaluate data, using advanced feature generation and categorisation techniques, in order to stay at the forefront of innovation. Analyse tabular and non-tabular data, such as text, logs, or time ...

expertise to make sense of complex data, working in close collaboration with our business partners. This role will primarily focus on feature engineering and insight generation from new types of data and the development of machine learning models to address critical business challenges in underwriting. We are interested … Develop and maintain the machine learning models which define our competitive advantage in the financial services market. Explore and evaluate data, using advanced feature generation and categorisation techniques, in order to stay at the forefront of innovation. Analyse tabular and non-tabular data, such as text, logs, or time ...