1 to 25 of 38 Feature Engineering Jobs in the UK

secure AI/ML architectures aligned with enterprise strategy, Omnia principles, and hybrid cloud environments. Design end-to-end AI solutions, including data pipelines, feature engineering, model development, deployment, and operational monitoring. Provide architectural leadership across the full lifecycle, ensuring alignment with DevSecOps, security, and engineering best … practices. Establish standards for model performance monitoring, reliability, risk management, and continuous improvement (MLOps). Collaborate with Enterprise Architecture, engineering leaders, and Army/MoD stakeholders to integrate AI capabilities into existing platforms and services. Evaluate and recommend AI platforms, tools, and frameworks to enable scalable experimentation and production ...

realise the full potential of an AI enabled future. Key Responsibilities Build and ship production ready AI/ML features-from data ingestion and feature engineering to model training, evaluation, and deployment Develop LLM/GenAI solutions (prompt engineering, tool use, guardrails) and RAG pipelines (chunking, embeddings … efficient memory/latency tuning Build and maintain MLOps/LLMOps workflows-CI/CD for models and prompts, model registry/versioning, feature stores, and automated promotion across environments Instrument observability for data, models, and prompts (telemetry, metrics, traces, dashboards, alerts); implement A/B tests and online ...

driven decision making across complex digital transformation programmes. Working with modern data platforms, high-quality datasets, and advanced AI frameworks, you will collaborate with engineering, product, and analytics teams to build models that are accurate, explainable, and scalable. The role empowers you to shape end-to-end analytical ecosystems … Collaborate with data engineers, analysts, product managers, and domain experts to translate business requirements into modelling tasks * Build end-to-end ML pipelines, including feature engineering, preprocessing, and deployment-ready outputs * Apply advanced techniques such as NLP, time series forecasting, anomaly detection, optimisation, or LLM/GenAI methods ...

complex digital transformation programmes. With access to modern data platforms, high quality datasets, and advanced statistical and AI frameworks, you will work closely with engineering, product, and analytics teams to build solutions that are accurate, explainable, and scalable. This role empowers you to shape end to end analytical ecosystems … data engineers, analysts, product managers, and domain experts to translate business requirements into well defined modelling tasks Build end to end ML pipelines-from feature engineering and preprocessing to deployment ready model outputs Apply advanced techniques such as NLP, time series forecasting, anomaly detection, optimisation, or LLM/ ...

making across complex digital transformation programmes. With access to cutting edge AI frameworks, high performance computing environments, and modern data platforms, you will guide engineering and data science teams in building secure, scalable, and ethical AI systems. This role empowers you to shape end to end AI ecosystems-accelerating … more intelligent, AI enabled future. Key Responsibilities Define the enterprise AI architecture vision and reference patterns; align them to business goals, risk posture, and engineering standards across cloud and hybrid environments Design secure, scalable AI solutions covering data ingestion, feature engineering, model training, inference, and continuous feedback ...

making across complex digital transformation programmes. With access to cutting-edge AI frameworks, high-performance computing environments, and modern data platforms, you will guide engineering and data science teams in building secure, scalable, and ethical AI systems. This role empowers you to shape end-to-end AI ecosystemsaccelerating delivery … more intelligent, AI-enabled future. Your responsibilities: Define the enterprise AI architecture vision and reference patterns; align them to business goals, risk posture, and engineering standards across cloud and hybrid environments. Design secure, scalable AI solutions covering data ingestion, feature engineering, model training, inference, and continuous feedback ...

Description: SAS Model Development & Enhancement (SAS Model Developer) 1. Design, develop, and implement statistical, pricing, or predictive models using SAS. 2. Build modelling datasets, feature engineering pipelines, and variable transformations. 3. Implement model logic, segmentation rules, scoring code, and scenario simulations. 4. Translate business/model requirements into … Convert prototype/model logic into production-ready SAS code. 2. Support scoring implementation, versioning, and production monitoring. 3. Work with IT/Data Engineering teams to deploy models in controlled environments. Required Skills & Experience 5-10+ years of SAS programming experience. Hands-on experience in: 1. ...

science and advanced analytics solutions that translate complex business problems into scalable, secure, and high performance data products. This role bridges business stakeholders, data engineering, data science, and IT architecture teams, ensuring solutions are production ready and aligned with enterprise standards. Your Responsibilities Solution & Data Model Design 1. Solution … Design & Architecture Design end to end data science solutions including data ingestion, feature engineering, model development, deployment, and monitoring Define logical and physical architectures for analytics platforms, ML pipelines, and AI products Ensure solutions are scalable, reusable, secure, and cost effective Select appropriate ML/AI techniques (e.g. ...

relevant frameworks Build APIs and integration layers for embedding AI into payments systems Work with data pipelines (real-time + batch) Implement data preprocessing, feature engineering, and model evaluation 4. AI MLOps & Deployment Set up and manage ML lifecycle using MLOps frameworks Implement: Model versioning CI/… detection Governance and audit controls Ensure production-grade deployment with compliance considerations 5. Stakeholder & Delivery Management Interface with product teams, risk teams, operations, and engineering Lead POCs and scale into production Support proposal creation and AI solution articulation for clients Domain 10+ years of experience in Banking/FinTech ...

looking for We are only interested in candidates who can demonstrate the following essential experience: Strong experience in data engineering, including building and maintaining data pipelines and data platforms Proficiency in Python and SQL Experience working with cloud platforms such as AWS or Azure A good understanding of data … sponsorship for this role. Applicants must have the right to work in the UK at the time of application. Job description Job responsibilities Data Engineering and Architecture In collaboration with the Head of Data, Digital and Technology and the Business Transformation Lead, to take the lead in developing BrisDocs ...

Learning models (e.g., classification, regression, clustering, deep learning) to solve complex business problems. Data Wrangling & Analysis: Perform comprehensive Exploratory Data Analysis ( EDA ), data cleaning, feature engineering, and transformation on large, complex, and sometimes unstructured datasets. Coding & Automation: Write production-quality, highly efficient, and scalable code primarily in Python ...

models to drive value from operational data and build scalable solutions adaptable across business units. Key Responsibilities: Build predictive & prescriptive models Perform EDA and feature engineering Collaborate with data engineering teams Translate insights into business impact Deploy, monitor, and optimize models Create reusable, scalable AI solutions Requirements ...

Learning Engineer to design, build, and productionise machine learning models for our innovative matching platform. You will work across the entire ML lifecycle, from feature engineering to deployment automation, ensuring the optimisation and explainability of inference processes. Collaborating closely with data scientists and product teams, your role will … focus on enhancing MLOps practices, ensuring high standards of security, performance, and compliance. Responsibilities Build and maintain scalable feature pipelines within data lakehouse architectures. Develop fallback feature flows and implement robust data quality checks. Develop ranking, scoring, and entity-similarity models for the matching platform. Use modern ...

accessible for self-service analytics and AI/ML model consumption.Collaborate with ML Ops and Data Science teams to define data product standards and feature engineering patterns.Evaluate and lead adoption of emerging Azure and Databricks capabilities - including Microsoft Fabric, OneLake and DirectLake - where they advance the data architecture.Drive … management across business, IT and compliance teams. Excellent communication, collaboration and influencing skills at all levels of an organisation. Experience leading data architecture and engineering teams in an enterprise environment. Ability to define and implement a data strategy aligned with business objectives. Proven track record of delivering enterprise-scale ...

Join our dynamic team and be part of building a greener, cleaner future! About the Role : The Technical Delivery Lead is a hands-on engineering role responsible for designing, building, and optimising solutions across Microsoft Fabric, Azure Integration Services (Logic Apps & Function Apps), Power Platform, and Power BI. This … role brings together technical engineering and strategic influence, contributing to technology direction, security standards, and architectural best practices in alignment with the Head of IT. The ideal candidate is a highly capable technical lead with strong data engineering and security awareness, who enjoys solving complex problems and delivering ...

tangible mark on how people develop their careers. This is a role for someone who's done real AI work inside a high-calibre engineering organisation and worked closely with product. What you'll do Design and deploy LLM/RAG systems (GPT, Claude, Gemini) powering content generation, semantic … model in a notebook you've taken it to real users, measured it, and iterated. You've done this inside organisations that take engineering seriously. Whether that's a top-tier tech company, a defence/intelligence-grade AI startup, or a fast-scaling data-led business you know ...

e.g., Mar's Simpel or similar) and improve performance and reliability Implement data modeling, transformation, and orchestration patterns aligned with best practices Apply data engineering fundamentals including partitioning, indexing, caching, cost optimization, and performance tuning Collaborate with upstream and downstream teams to ensure data quality, reliability, and SLAs Architecture … Evaluate tools and frameworks with a build vs. buy mindset, recommending pragmatic solutions Team & Delivery Responsibilities Act as a technical anchor for a data engineering team Provide technical guidance, code reviews, and mentoring to engineers Own delivery for assigned data products or pipelines — from design through deployment Collaborate with ...

conversational analytics products • Develop LLM-driven systems including fine-tuning and prompt pipelines • Apply statistical modelling and quantitative analysis to financial datasets • Conduct feature engineering, model evaluation, and experimentation • Work with engineering teams to integrate models into AWS-based platforms • Support development of AI systems used ...

skills, and a results-driven mindset Role responsibilities: Build, tune, and maintain predictive and ML models using AWS SageMaker Analyse large datasets and perform feature engineering to improve model performance Run experiments, test hypotheses, and optimise models for accuracy and value Monitor model performance and manage retraining over ...

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. ...

including Microsoft Fabric, Azure AI and ML tooling Key skills Strong experience with Microsoft Fabric (Data Factory, Lakehouse, OneLake, Pipelines, SQL Warehouse) Solid data engineering background using SQL, Python, PySpark, Delta Lake E xperience building and optimising end-to-end data pipelines CI/CD experience for analytics … hybrid platforms to a modern Fabric setup Supporting BI teams with semantic models, datasets and performance optimisation Enabling ML and MLOps workflows, from feature engineering to deployment Working closely with data scientists, analysts, IT and the wider business Embedding data quality, governance and best-practice standards Providing technical ...

investors and data-driven organisations. With data and AI at the core of their product, they are scaling quickly and investing heavily in their engineering and AI capabilities. You will join a collaborative, fully remote team that values transparency, curiosity and speed of execution. The Role You will … small, focused AI team and work closely with engineering to design, build and productionise new AI features across the platform. Key responsibilities include: Designing, training and deploying AI and ML models on complex, large-scale data. Building GenAI solutions using techniques such as RAG and frameworks like LangChain ...

proof-of-concept development using Databricks notebooks, analytical outputs and machine learning techniques to validate product concepts Own technical product definition, including data structures, feature sets, scoring methodologies, model architectures and delivery formats in collaboration with the Product Team Define and monitor technical success metrics (data coverage, refresh latency … needs into technical product requirements Support rapid prototyping and proof-of-concept development using Databricks notebooks and analytics outputs Define product-level data structures, feature sets, scoring outputs and delivery formats in collaboration with the Product Team Product Launch & Performance responsibilities: Define technical success metrics (data coverage, refresh latency ...

translate insights into business impact. Key Responsibilities Design and implement predictive and prescriptive models for MRO AI Solutions. Perform exploratory data analysis and feature engineering. Collaborate with Data Engineers to ensure data readiness for modeling. Communicate findings and recommendations to business stakeholders. Continuously improve models based on feedback ...

translate insights into business impact. Key Responsibilities Design and implement predictive and prescriptive models for MRO AI Solutions. Perform exploratory data analysis and feature engineering. Collaborate with Data Engineers to ensure data readiness for modeling. Communicate findings and recommendations to business stakeholders. Continuously improve models based on feedback ...