22 of 22 Permanent Feature Engineering Jobs in England

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 ...

Work with large datasets to build predictive and analytical models • Collaborate with data scientists to transition models from research to scalable production systems ML Engineering and Deployment • Design and maintain machine learning pipelines and model deployment infrastructure • Implement automated model training, validation, and monitoring processes • Ensure models are scalable … reliable, and performant in production environments Data Engineering and Infrastructure • Work with data engineers to build efficient data pipelines and feature engineering processes • Optimise data workflows to support model development and experimentation • Contribute to the development of robust ML infrastructure and tooling Collaboration and Product Integration • Work ...

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 ...

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 ...

pure management role. They are looking for a strong Data Scientist first, someone comfortable across the full lifecycle from ingestion and feature engineering through to modelling and deployment. Their data estate has evolved over time and is currently spread across multiple siloed systems with differing structures and standards. … systems, and proactively define a clear data and AI roadmap. You will lead a Data team of 5-6 across Data Science and Data Engineering, raising standards while still contributing directly to predictive models and AI driven tools. This is a genuine opportunity to bring structure, clarity and technical ...

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 ...

Engineer who will report to the Director of AI Engineering. This role will be a subject matter expert for applied AI within our Engineering organisation. This role suits a hands‐on team lead who builds and ships customer‐facing, production‐grade AI, solving complex problems, shaping long‐term strategy …/CD; fluent with AI coding assistants (Cursor, Copilot, Claude Code) and accountable for outcomes Applied LLMs: Claude or similar; RAG, agents, prompt engineering; embeddings + vector DBs; hybrid search; evaluation/benchmarking Data engineering: ETL; SQL/NoSQL design; data sourcing/ingestion with governance and quality ...

great opportunity. The Role Youll work directly with senior leadership to design, develop, and deliver AI solutions across the business. This includes hands on engineering, stakeholder engagement, and contributing to responsible AI practices. There is also a clear development pathway into a future Lead AI position. What Youll … Doing Designing and delivering AI powered solutions, including generative AI models Engineering and preparing data for AI model development Integrating AI models into existing tech and cloud infrastructure Ensuring compliance with data protection and sector regulations Translating complex AI concepts into clear language for non technical teams Identifying innovation ...

every step of the way. Ready to shape your future? Explore opportunities with us today. We are looking for an AI MLOps Lead – Payments Engineering for our well-known client. Location: London, UK Type of Work: Hybrid (2-3 days from the office in a week) Employment Type … 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: o Model versioning ...

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 ...

models for user personalisation, including recommendation systems, ranking models, user segmentation, and content understanding, with a strong focus on TensorFlow-based development. Data Pipeline Engineering: Build and maintain scalable data pipelines to support feature engineering and model training across large structured and unstructured datasets, leveraging cloud‐native … relevance. Experimentation: Lead A/B testing and offline experimentation to evaluate model performance and guide ongoing improvement. Cross‐Functional Collaboration: Work closely with engineering, product, data, and research teams to ensure ML solutions align with product and business goals. Research & Innovation: Stay informed on advances in machine learning ...

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 ...

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 ...

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 ...

across the organisation. Key Actions Contribute to Advanced AI and Machine Learning Development: Support delivery of the machine learning lifecycle, including data ingestion, exploration, feature engineering, modelling, evaluation, and assisting with deployment and monitoring Help Drive Innovation and Shape AI Opportunities: Identify where analytics or machine learning … projects, delivering high-quality outputs that support commercial or customer outcomes Engage Stakeholders and Support AI Adoption: Work with teams across actuarial, operations, product, engineering, commercial, and clinical areas to understand needs and help shape solutions Contribute to Developing the Data Science Community: Participate in knowledge sharing sessions, workshops ...

ownership and production-grade model deployment. The Role As Senior Data Scientist, you will: Own end-to-end ML solutions -from problem framing and feature engineering to deployment, monitoring, and governance Translate business objectives into modelling strategies aligned to risk appetite and operational constraints Build and deploy models … using Python, SQL, and AWS (SageMaker or equivalent) Partner closely with Engineering, Data, and Risk/Financial Crime teams to ensure robust, production-ready solutions Establish monitoring frameworks for performance, drift, and retraining Drive clear documentation, traceability, and governance appropriate for a regulated environment This role requires someone ...

will: Lead and develop a growing Data Science team, setting standards and delivery cadence Own end-to-end ML solutions -from problem framing and feature engineering to deployment, monitoring, and governance Translate business objectives into modelling strategies aligned to risk appetite and operational constraints Build and deploy models … using Python, SQL, and AWS (SageMaker or equivalent) Partner closely with Engineering, Data, and Risk/Financial Crime teams to ensure robust, production-ready solutions Establish monitoring frameworks for performance, drift, and retraining Drive clear documentation, traceability, and governance appropriate for a regulated environment This role requires someone ...

platform. The Opportunity This is a hands-on leadership role where you will lead a team of 5–6 across Data Science and Data Engineering while remaining technically involved in modelling, machine learning deployment and architectural decisions. The platform runs on AWS and includes large-scale crawlers ingesting significant … Designing and improving a scalable AWS based data platform Rationalising fragmented data across multiple systems Leading the full machine learning lifecycle from ingestion and feature engineering through to deployment Improving neural network and rule based matching systems used to identify the same entities across different sources Embedding robust ...

maintain high-performance C++ and Python components used for modelling, simulation, and live trading. Design scalable Linux-based data and compute architectures , including feature engineering and large dataset processing. Support the creation of robust backtesting environments , ensuring accuracy, reproducibility, and efficiency. Collaborate with investment teams to enhance portfolio … experience contributing to scalable, modular research or trading systems. Strong quantitative background with a degree in a highly technical field (Computer Science, Mathematics, Physics, Engineering, Statistics, or related STEM discipline). Excellent problem-solving skills and the ability to work closely with front-office teams. Bonus/Preferred Skills ...

prices and select customers, beyond traditional pricing approaches. Work with very large, messy datasets across multiple product lines, leading data exploration, cleaning and feature engineering. Deploy models into production using Python, SQL, Azure ML and an internal deployment platform. Collaborate with pricing, risk and product stakeholders to test ideas … CatBoost, and an interest in geospatial or graph based modelling. Confidence working with large scale, messy, multi source datasets, including complex data cleansing and feature engineering. Experience solving pricing, risk or customer prediction problems and understanding how models drive profit, loss and customer outcomes. How To Apply ...