1 to 25 of 87 Permanent 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 ...

univariate time series. This is structured, high-dimensional, real-world forecasting at a level that actually moves enterprise decisions. Own the full modelling lifecycle. Feature engineering, model selection, validation strategy, uncertainty quantification, production deployment. You'll make the calls and live with the results - which means … goal is for your models to inform what Monq recommends inside live procurement negotiations. Getting there requires working closely with product and engineering - translating probabilistic outputs into something a procurement professional can act on in the moment, not just admire in a dashboard. Bridge research and engineering ...

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 ...

predictive models embedded in automated workflows, your work will directly influence business operations at scale. You’ll operate at the intersection of modelling, engineering, and intelligent automation. 🌐 What You’ll Do 🧠 Model Development & AI Systems Design Design and train predictive models using AWS SageMaker Develop LLM-powered systems … Bedrock (including Claude integration) Build RAG pipelines combining structured and unstructured data Develop evaluation frameworks for accuracy, bias, and robustness Apply best practices in feature engineering and experimentation 🤖 AI Agent & Workflow Integration Architect reasoning agents using advanced foundation models Use code-generation tooling for automation logic and integration ...

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 … Your responsibilities: (Up to 10, Avoid repetition) 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 ...

integrate LLM-powered solutions for tracing, evaluation, and production monitoring Implement robust experiment tracking, model versioning, and reproducibility practices with full audit trails Design feature engineering approaches and contribute to feature store development Support production models through monitoring, performance analysis, and continuous improvement Apply responsible AI practices … quality controls Experience with experiment tracking and MLOps tooling (MLflow, Weights & Biases, Data Version Control) Experience developing LLM/GenAI applications, including prompt engineering and RAG architectures It Would Be Great If You Also Had Experience In Some Of These, But If Not Well Help You With Them Experience ...

Bring: We are seeking a seasoned professional who is excited by the unique challenges of AI data. Must-Have Skills: Extensive Data Engineering Experience: Proven track record (3+ years) in designing, building, and maintaining large-scale data pipelines and data warehousing solutions. Cloud Platform Mastery: Expert-level proficiency with … Kafka, and data orchestration tools such as Apache Airflow or Prefect. ML Data Acumen: Solid understanding of data requirements for machine learning models, including feature engineering, data validation, and dataset versioning. Vector Database Experience: Practical experience working with vector databases (e.g., Pinecone, Milvus, Chroma) for embedding storage ...

data‐driven product initiatives. This role focuses on preparing high‐quality datasets, enabling machine‐learning workflows, and building scalable pipelines using modern data‐engineering tools. You will work closely with engineering, product, and business teams to deliver reliable, production‐ready data solutions across cloud environments. Hybrid working … your home; noting that you will be unable to work at home 100% of the time. Your Role: Perform data cleaning, transformation, and feature engineering to support ML and analytics use cases. Build and optimize data pipelines and automated workflows using Python, Scala, PySpark, and relevant ML libraries. ...

cover Key Responsibilities Training Delivery & Facilitation Deliver high-quality, hands-on training in AI, Machine Learning, and related domains (e.g. deep learning, NLP, data engineering) Facilitate practical labs, workshops, and scenario-based exercises using real-world datasets and problems Adapt delivery style to suit mixed audiences (engineers, analysts, operators … development and deployment in real-world environments Proficiency in Python and common ML libraries (e.g. TensorFlow, PyTorch, Scikit-learn) Experience with data pipelines, feature engineering, and model evaluation Exposure to areas such as: Deep Learning Natural Language Processing (NLP) Predictive analytics MLOps and model lifecycle management Familiarity with ...

passionate about architecting modern data platforms that power real AI at scale — this opportunity is for you. This is more than a data engineering role! You will define and scale the organisation’s data platform to power advanced analytics, machine learning, and AI-driven products. Combining deep technical expertise … time pipelines Champion data modelling standards, semantic layers, and metric governance 🤖 AI & ML Enablement at Scale Architect data foundations supporting the ML lifecycle Design feature stores, embedding pipelines, and AI-ready datasets Enable MLOps workflows (data versioning, monitoring, retraining triggers) Support production inference (batch and real-time) Evaluate ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

defining how models should be evaluated, monitored and governed across their lifecycle. You will work closely with domain experts, the existing AI team, data engineering and product teams to ensure our AI systems behave consistently, accurately and responsibly. This is not an LLM-engineering or roadmap ownership role. … based systems. Standardise how experiments are structured, documented and compared across teams. Create reproducible workflows for testing prompts, model versions, embeddings and classical feature models. Ensure statistical soundness and methodological rigour in all experiments. You define “what goods like” in experimentation. Own the Evaluation Framework for Model Behaviour: Build ...

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 ...

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 ...

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 ...