1 to 25 of 33 Permanent Feature Engineering Jobs in England

Machine Learning, Artificial Intelligence, Predictive Analytics, Statistical Modelling, Python, SQL, PySpark, Azure, Databricks, MLflow, GitHub Actions, CI/CD, Time Series Forecasting, Propensity Modelling, Feature Engineering, Data Engineering, Azure Machine Learning, Azure Data Platform, Experiment Design, A/B Testing, Hypothesis Testing, Predictive Modelling, Customer Analytics, Marketing … that improve customer outcomes, enhance commercial performance & support strategic business decision making. Own the complete analytical lifecycle, from initial problem definition & exploratory analysis through feature engineering, model development, deployment, operational monitoring & continuous optimisation. Build sophisticated statistical & machine learning models covering areas such as: * Customer Lifetime Value * Customer Propensity ...

Consulting Firms. Our Solutions & AI Labs (SAIL) practice is looking for an experienced Senior Manager to lead consultancy engagements and grow our AI & Solutions Engineering capability. The Forward Deployed AI & Solutions Engineer role will lead the delivery of AI-enabled solutions embedded in our clients’ teams, combining deep technical … solutions directly embedded into business processes. We support clients across a number of industries and offer deep expertise in AI/ML, Cloud, Platform Engineering, and Managed Solutions. What you will be doing As a Senior Manager in SAIL, you will own and lead consultancy engagements ...

winning the work, through to delivery governance and team leadership. You will bring SME-level depth in at least one of AI/ML Engineering, Platform Engineering, or Software Engineering, and apply it to create real, lasting value for our clients. Although we do not expect … Specialism You will bring expert-level knowledge in at least one of the following domains, and working knowledge across the others: Agentic AI & LLM Engineering Design and build production-grade agentic systems using major LLM SDKs and agent frameworks; deep knowledge of RAG, MCP servers and prompt engineering ...

Principal, AI/ML Engineer leads the design, delivery and continuous improvement of AI and ML solutions within their area of responsibility, aligning engineering activities to the Bank’s broader AI strategy and long‐term objectives. The role combines strong technical leadership with a clear focus on delivery outcomes … operates with a clear understanding of the wider technology landscape, contributing to strategic direction while taking accountability for translating this into practical, high‐quality engineering outcomes across squads. AI/ML Engineering Direction Leads the definition and execution of AI/ML engineering direction across ...

Experience designing and analysing A/B tests, defining success metrics, understanding selection bias, and evaluating ranking systems using both online and offline methodologies. Feature Engineering & Applied Machine Learning : Strong background in feature engineering, tabular machine learning, calibrated scoring systems, and production use of models such … Entity‐Aware Ranking : Ability to incorporate relationships between companies, sponsors, lenders, law firms, instruments, documents, and events into ranking and recommendation decisions. Production ML Engineering : Experience building feature pipelines, online and batch inference systems, low‐latency ranking services, experimentation platforms, and monitoring for production machine learning systems. Strong ...

collaboratively across a fast-moving technology company, turningcutting-edgeresearch into practical, scalable solutions.This role reportsto the AppliedAILead. Responsibilities Lead data identification, cleaning, enrichment, preprocessing,feature engineering,andexploratoryanalysis to ensure fitness for AI workflows and to inform modelling and business decisions. Build, tune, andoptimisemachine learning, deep learning, and generative … models,leveragingboth established andcutting-edgetechniques. Designand buildLLM-powered systems— RAG pipelines, prompt and context engineering, fine-tuning, structured outputs, function calling, context-window management,and secure model integration;selectingappropriately between standard and reasoning (test-time-compute) models, balancing capability against latency and cost. Designand buildagentic AI systems— tool-calling ...

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 ...

generating, deploying and continuously improving bespoke machine learning models for every customer. You'll work with large-scale behavioural event data, applying machine learning, feature engineering and experimentation to improve prediction accuracy and drive measurable customer outcomes. Working alongside highly experienced founders and recognised experts in machine learning … business. Skills & Experience Strong commercial experience building machine learning models in Python Excellent understanding of predictive modelling, classification or propensity modelling Experience with feature engineering, model evaluation and experimentation Comfortable working across the full machine learning lifecycle Experience working with cloud platforms and modern ML tooling Curious ...

technical leadership across the full lifecycle of machine learning systems powering a new matching platform. This role is accountable for defining ML architecture, establishing engineering standards, driving MLOps maturity, and ensuring that our models are scalable, secure, explainable, and governed to enterprise‐grade standards. You will contribute … compliance framework. This is a highly hands‐on, highly technical, principal‐level role that combines architectural vision with deep practical expertise in ML engineering and AWS-native MLOps. Key Responsibilities Technical Leadership & Architecture Define the end‐to‐end ML architecture for the matching platform, including data pipelines, model training ...

Work With Building and deploying AI and Machine Learning solutions Designing and implementing end-to-end AI and ML pipelines, from data acquisition and feature engineering through to deployment and optimisation Developing scalable ML pipelines and engineering frameworks AI model training, evaluation, optimisation and deployment Cloud-native … stakeholders to understand business challenges and translate requirements into AI solutions Working closely with technical and non-technical teams throughout programme delivery Cross-functional engineering, architecture and data teams Agile software development and delivery methodologies Exploring and adopting emerging AI technologies and approaches to drive innovation and business value ...

concept into production environments Develop scalable ML pipelines using AWS cloud technologies Implement MLOps and LLMOps best practices for deployment, monitoring and governance Support feature engineering, model optimisation and performance monitoring Apply responsible AI principles, including model explainability and fairness Present findings and recommendations to technical … tools such as MLflow, Weights & Biases or Data Version Control Experience designing, testing and evaluating machine learning experiments Knowledge of Generative AI, LLMs, prompt engineering and RAG architectures Experience with LLMOps tools such as LangChain, LangSmith or LangGraph Strong understanding of model monitoring, validation and governance Excellent communication ...

full delivery lifecycle, from early ideas through to production systems that create measurable impact for our customers and the business. As MLOps Engineering Manager, you will help shape how we build, deploy and operate machine learning products at scale, working closely with ML Engineers, Data Engineers, Software Engineers, Data … operation of machine learning products, ensuring models and AI systems are production‐ready, observable and able to support Trainline’s growth. Partner closely with engineering, data science, product and data teams to bring strong engineering standards into machine learning delivery, while recognising the specific challenges of data ...

such as GANs and VAEs. Proven experience applying these techniques to real-world tasks (text, code, image, multimodal generation) and familiarity with advanced prompt engineering, structured outputs, function/tool calling, and orchestration frameworks like LangChain, LangGraph, LlamaIndex, and Semantic Kernel. Hands‐on exposure to API-based LLM providers … project management skills, overseeing timelines, milestones, and deliverables; coordinating internal teams and clients to ensure project success. Data Understanding : Foundational grasp of data preprocessing, feature engineering, and data quality assurance processes to support AI model requirements. Responsible AI & Governance : Awareness of AI governance, safety, and compliance considerations—data ...

solution development—from data exploration and experimentation through to deployment, monitoring, and continuous optimisation. They will work closely with cross-functional teams including data engineering, infrastructure, DevOps, and product management to translate business needs into robust, reusable machine learning architectures. Key Responsibilities Design and deploy large-scale machine learning … systems into production using modern engineering practices and tools. Build and maintain core ML infrastructure, including pipelines for feature engineering, model training, evaluation, deployment, and monitoring. Automate the full AI/ML lifecycle, covering data ingestion, experimentation, tuning, and visualisation. Collaborate with product teams to convert business ...

value creation initiatives to be backed by reliable, real-time data. When companies partner with JMAN, we combine our data science and data engineering expertise with our deep commercial understanding to deliver tangible, high-value outcomes at pace. Founded in 2010, JMAN has a global footprint with offices … Data Science or applied machine learning, ideally in consulting, high‐growth, or commercial environments. Strong applied machine learning expertise, including: Model selection and development Feature engineering Evaluation and validation Deployment into production environments Demonstrated experience taking models beyond experimentation into operational, business‐critical systems. Strong background in statistics ...

scale it takes to feed the nation. The level of data, transactions and variety involved. Then you’ll realise this is a modern software engineering environment, because it has to be. We’ve made significant investment in the standards and principles that shape how we work. We iterate, learn … will partner with Data Scientists and Software Engineers, to ensure the system is reliable, performant and production‐ready. You will also contribute to engineering excellence, across Machine Learning and MLOps, by driving best practices, mentoring other engineers, and shaping the technical direction of data and ML workflows across ...

will partner with Data Scientists and Software Engineers to ensure the system is reliable, performant, and production‐ready. You will also contribute to engineering excellence across Machine Learning and MLOps by driving best practices, mentoring other engineers, and shaping the technical direction of data and ML workflows across … domain. Key Responsibilities Lead the design and build of high‐quality, scalable, and reusable Machine Learning systems using Sainsbury’s engineering standards and best practices. Design and optimise Machine Learning modules for Advert classification, applying the ML‐Lifecycle and best practice in model selection and development. Lead the design ...

Bedrock. What you’ll do Partner with stakeholders across the business to explore high‐impact opportunities. Own the full lifecycle: problem framing, data discovery, feature engineering, modelling, evaluation, deployment, monitoring, and iteration. Build and productionize LLM features where appropriate (retrieval‐augmented generation, evaluation, safety guardrails, cost/latency … Contribute to DS/ML standards: experimentation, model governance, documentation, and reproducibility. Mentor junior scientists, work with external contractors and collaborate closely with data engineering on pipelines and data quality. What you’ll bring A proven track record delivering projects in a Data Science or AI. Experience deploying models ...

play a key role in designing, developing and deploying AI and Machine Learning solutions, working across the full ML lifecycle from data preparation and feature engineering through to model deployment and optimisation. Working with Python, SQL and leading ML frameworks, you'll build scalable pipelines, develop and evaluate ...

providing advanced insights to improve model accuracy and reliability. Apply deep learning frameworks and libraries such as scikit-learn, XGBoost, and LightGBM to enhance feature extraction and classification performance in vision projects. Guide team members in adopting best practices for code quality, model deployment, and performance optimization, leveraging tools … Computer Vision Algorithms. 2. - Solid Experience With Python, Tensorflow, Pytorch, And Scikit-Learn For Model Development And Evaluation. 3. - In-Depth Knowledge Of Data Engineering Tools Including Apache Spark, Kafka, and Airflow For Scalable Ml Workflows. 4. - Strong Understanding Of Model Evaluation Metrics Such As Roc/Auc, Precision ...

limitations, and model performance. Build and productionize analytics that advance intraday/EoD automation (services, APIs, pipelines) with clear SLOs/SLA, observability, reliability engineering practices, and tight integration into trading/risk platforms. Provide technical leadership and mentorship; conduct code/method reviews, establish research engineering best … resourcing via data‐driven analysis, scenario studies, and clear articulation of trade‐offs. Lead development of ML/AI solutions end‐to‐end (feature engineering, model training/validation, MLOps, monitoring and drift management) with rigorous controls, documented governance, and demonstrable business value. Uphold standards for documentation, reproducibility ...

Development: Design, train, and optimise machine learning models focused on user personalisation, encompassing recommendation engines, ranking algorithms, user segmentation, and content analysis. Data Pipeline Engineering: Construct and maintain robust and scalable data pipelines for feature engineering and model training utilising both structured and unstructured large-scale datasets. … with a team retrospective. Global OTT Technology Our team develops and supports market-leading video streaming services, underpinned by state-of-the‐art engineering principles. We do this at huge scale: for over 50 million customers globally, spanning NBCUniversal Peacock in the US and Sky, NOW and SkyShowtime across ...

client engagements to assess and remediate financial crime risk across payments and banking environments. Drive data diagnostics and analytical assessments including data profiling, feature engineering, exploratory analysis, and performance benchmarking. Evaluate technology solutions for AML monitoring, customer risk scoring, and customer screening, and support vendor selection and integration … processes. Collaborate with client data engineering teams to advise on data pipelines, schema design, and pipeline infrastructure needed for analytic and monitoring use cases. Translate complex technical concepts into clear, executive‐level recommendations and actionable roadmaps. Business Development and Lead Generation Maintain and develop a personal profile ...

Purpose of the Role To drive technical excellence and innovation by leading the design and implementation of robust software solutions, providing mentorship to engineering teams, fostering cross‐functional collaboration, and contributing to strategic planning to ensure the delivery of high‐quality solutions aligned with business objectives. Accountabilities Provision … guidance and expertise to engineering teams to ensure alignment with best practices and foster a culture of technical excellence. Contribution to strategic planning by aligning technical decisions with business goals, anticipating future technology trends, and providing insights to optimise product roadmaps. Design and implementation of complex, scalable, and maintainable ...

solutions. In this key role you’ll turn fraud, credit risk, financial crime, compliance and regulatory needs into clear technology requirements, providing mentorship to engineering teams, fostering cross‐functional collaboration, and contributing to strategic planning to ensure the delivery of high‐quality solutions aligned with business objectives. … customer onboarding, and real‐time decision management Some other highly valued skills may include: Experience operationalising AI/ML solutions including model lifecycle management, feature engineering, vector databases, RAG architectures, AI governance and production‐scale MLOps Experience building modern data platforms incorporating data mesh/Lakehouse architectures, real ...