1 to 25 of 50 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 ...

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 ...

applications, integrating AI models into business systems, and supporting the full AI development lifecycle. The ideal candidate is passionate about modern AI technologies, software engineering best practices, and delivering reliable AI solutions at scale. Key Responsibilities AI Application Development Design, build, and maintain AI-powered applications and services. Develop … Retrieval-Augmented Generation (RAG) systems and AI agents. Integrate foundation models and APIs into enterprise applications. Create reusable AI components and frameworks. Machine Learning Engineering Train, fine-tune, evaluate, and deploy ML models. Develop feature engineering and model evaluation pipelines. Implement model monitoring and performance tracking. Optimize ...

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 ...

Design, build, maintain, and improve lifetime value prediction models across ARQ’s countries and acquisition channels. Work closely with Growth & Marketing, Product, Finance, Data Engineering, and Engineering teams to understand business needs and translate them into modelling solutions. Help evaluate acquisition quality by channel, campaign, geography, customer segment … reliability, and business impact over time. Create clear frameworks and metrics that help teams understand the trade offs behind growth decisions. Partner with Data Engineering and Engineering teams to productionise models, pipelines, and reporting where needed. Bring a pragmatic approach to modelling, balancing technical depth with commercial impact. ...

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 ...

proving what s possible with our data and handing those proofs to engineering. As a Data Scientist at Third Bridge, you will be the engineering-focused prototyping engine within a high-impact data team. Working alongside our senior analytics capability and reporting to the Principal Data Architect, your primary … proof-of-concept (PoC) solutions from new dataset pipelines and ML models to AI-powered internal tools and hand off successful proofs to the engineering team for production integration. You will work with Third Bridge s rich proprietary data assets: transcripts, events, interaction data, commercial performance metrics, and content ...

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 ...

value-based decisioning logic - incorporating CLV and long-term customer value into prioritisation, not just short-term conversion Measurement frameworks and optimisation Define feature engineering requirements - knowing which signals, triggers, and contextual features drive predictive power in decisioning Architect real-time decisioning solutions integrating with … SNGCR01 Qualification Required - Technical Skills Advanced Python - writing clean, production-grade code, not just scripts (OOP, async, packaging, testing) Generative AI & LLM development - prompt engineering, fine-tuning, RAG pipelines, context window management Agentic AI frameworks - LangChain, LangGraph, AutoGen, or similar; building multi-step autonomous workflows Machine Learning - model development ...

Software Engineer II - Applied AI in the AI/ML for Engineering team at JPMorganChase, you'll be at the forefront of AI innovation, combining cutting-edge techniques with unique data assets to optimise business decisions and automate processes. This role offers a unique blend of artificial intelligence … software engineering, allowing you to advance engineering processes and build impactful products. In this role, you will leverage the latest research in Large Language Models, Natural Language Processing and statistical machine learning to build AI-powered products that automate processes and enhance decision-making. You will collaborate with ...

connect them with exceptional professionals for this role. JOB DESCRIPTION As a Software Engineer II - Applied AI in the AI/ML for Engineering team at JPMorganChase, you'll be at the forefront of AI innovation, combining cutting-edge techniques with unique data assets to optimise business decisions … automate processes. This role offers a unique blend of artificial intelligence and software engineering, allowing you to advance engineering processes and build impactful products. In this role, you will leverage the latest research in Large Language Models, Natural Language Processing and statistical machine learning to build AI-powered ...

NumPy, scikit-learn and relevant statistical or machine learning packages Hands-on experience with machine learning frameworks and approaches, including supervised learning, unsupervised learning, feature engineering, modelselectionand model validation Practical experience building Generative AI and LLM-based solutions, including prompt engineering, embeddings, vector databases, RAG, grounding, reranking ...

NumPy, scikit-learn and relevant statistical or machine learning packages Hands-on experience with machine learning frameworks and approaches, including supervised learning, unsupervised learning, feature engineering, modelselectionand model validation Practical experience building Generative AI and LLM-based solutions, including prompt engineering, embeddings, vector databases, RAG, grounding, reranking ...

NumPy, scikit-learn and relevant statistical or machine learning packages Hands-on experience with machine learning frameworks and approaches, including supervised learning, unsupervised learning, feature engineering, modelselectionand model validation Practical experience building Generative AI and LLM-based solutions, including prompt engineering, embeddings, vector databases, RAG, grounding, reranking ...

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 ...

implement robust evaluation frameworks to systematically assess and measure the performance of AI agents across key operational metrics. Practice advanced prompt and context engineering (e.g., Chain-of-Thought, ReAct, function calling/tool-use prompts), implement output validation and guardrails to reduce hallucinations. Deploy scalable AI services to cloud … LangChain, CrewAI, AutoGen, LangGraph, ADK). Experience with generative models (transformers, GANs/VAEs; diffusion models a plus). Strong understanding of data preprocessing, feature engineering, and evaluation techniques. Familiarity with cloud platforms (AWS, Azure, GCP) and containerization (Docker, Kubernetes). Strong communication skills for both technical ...