51 to 75 of 148 Feature Engineering Jobs in London

distributed computing (e.g., Spark, Dask) and ensure robust test coverage for production systems. Mentor junior team members and utilise model explainability methods to drive feature performance improvements. About You 5+ years of experience in designing, building and maintaining machine learning models to solve complex, large-scale business problems. Deep … statistics, plus supervised and unsupervised modelling techniques. Proven experience modelling complex interactions (e.g., cluster or network effects). Expertise in model explainability to tune feature engineering and understand feature interactions. Ability to write high-quality, production-grade Python code and collaborate effectively with non-technical stakeholders. Proficient ...

Your Impact Customer Onboarding & Engine Customization: Lead the technical deployment of our engines for new clients. Partner closely with customers, Customer Success, Product, and Engineering to adapt and customize ML models, ensuring they accurately reflect client-specific business logic, operational guardrails, and unique data nuances. Analytic Engine Development: Design … implement robust Machine Learning (LightGBM, XGBoost) and Deep Learning (PyTorch) models specifically focused on complex demand sensing and resolving cold-start forecasting problems. Feature Engineering Pipelines: Build and optimize scalable data pipelines leveraging a hybrid SQL/Python architecture (dbt, Snowflake) to extract rich, localized temporal signals from ...

innovation and transformation goals through GenAI/ML architectures. This role requires technologists with deep domain-specific expertise, able to address advanced concepts and feature designs. As part of the AGS organization, SSAs work with customers who have complex requirements that require expert-level knowledge to solve. You must … recommendation systems, or conversational AI.Ideally, you also have experience with large language models (LLMs), foundation models, retrieval-augmented generation (RAG), fine-tuning, prompt engineering, or agentic AI architectures. Hands-on experience with AWS AI/ML services such as Amazon Bedrock, Amazon SageMaker, Amazon Q, or Amazon Rekognition ...

applications in relevant domains like image recognition, natural language processing, or recommender systems. Data Science: Strong foundation in data science principles, including data analysis, feature engineering, model training, evaluation, and deployment. Programming Languages: Expertise in Python for data manipulation, model development, and scripting. Cloud Platforms: Experience with cloud … retail domain (e.g., text and image analysis , pricing models , recommender systems etc). Engineer and maintain data pipelines for data acquisition, cleaning, transformation, and feature engineering. Train machine learning models using various algorithms. Integrate machine learning models into our existing infrastructure and applications. Leverage existing LLMs based ...

/ML, including leading teams and projects from conception to production Strong foundation in AI/ML concepts and practical experience with data analysis, feature engineering, and model development Experience working with large, complex datasets and applying statistical analysis Hands‐on experience training, deploying, and maintaining machine learning ...

that turn real customer signals into timely actions and measurable outcomes allowing customers to amplify consumer voice for LLM visibility advantage. Partner across Product, Engineering, CS, and Commercial to make the human signal visible, actionable, and compounding in the product. Establish experimentation and measurement foundations: design how we test … into simple, trusted narratives that guide product and commercial decisions. Productionize and scale thoughtfully: ship durable data and model workflows in collaboration with the engineering team, ensure quality and monitoring, and document decisions so the system is reliable, explainable, and easy to evolve. What you’ll bring Track record ...

doing: Design, build and operate machine learning systems that support customer engagement, marketing effectiveness, pricing and commercial decision-making. Own the end-to-end engineering lifecycle of machine learning products, including data ingestion, feature engineering, deployment, monitoring and optimisation. Productionise advanced machine learning solutions and ensure they … closely with Applied Scientists to translate research and experimentation into scalable production systems. Help shape the future of our MLOps platform by contributing to engineering best practices, operational excellence and platform capabilities. Build reusable tooling, frameworks and infrastructure that accelerate machine learning delivery and reduce operational overhead. Influence technical ...

agent tools and APIs to connect agents with external services, databases, and internal systems, ensuring robust output parsing and error handling. Practice advanced prompt engineering and implement output validation and guardrails to reduce hallucinations. Design microservices-based architectures and orchestrate multi-step workflows; instrument agents for tracing, metrics … with agent frameworks such as LangChain, CrewAI, AutoGen, LangGraph. Knowledge of generative models including transformers, GANs, VAEs, and diffusion models. Understanding of data preprocessing, feature engineering, and model/agent evaluation techniques. Familiarity with cloud platforms and containerization technologies. Strong problem‐solving skills and ability to work independently ...

agent tools and APIs to connect agents with external services, databases, and internal systems, ensuring robust output parsing and error handling. Practice advanced prompt engineering and implement output validation and guardrails to reduce hallucinations. Design microservices-based architectures and orchestrate multi-step workflows; instrument agents for tracing, metrics … with agent frameworks such as LangChain, CrewAI, AutoGen, LangGraph. Knowledge of generative models including transformers, GANs, VAEs, and diffusion models. Understanding of data preprocessing, feature engineering, and model/agent evaluation techniques. Familiarity with cloud platforms and containerization technologies. Strong problem-solving skills and ability to work independently ...

Computer Science, Artificial Intelligence, or a related field (or equivalent experience) Strong foundation in AI/ML concepts and practical experience with data analysis, feature engineering, and model development Experience working with large, complex datasets and applying statistical analysis Hands-on experience training, deploying, and maintaining machine learning ...

Computer Science, Artificial Intelligence, or a related field (or equivalent experience) Strong foundation in AI/ML concepts and practical experience with data analysis, feature engineering, and model development Experience working with large, complex datasets and applying statistical analysis Hands-on experience training, deploying, and maintaining machine learning ...

projects end to end, from problem discovery and technical design through to delivery and measuring impact. Collaborate with Product, Compliance, Operations and other engineering teams while maintaining high standards for reliability, testing and code quality. We are fully aware that it is uncommon for a candidate to have … understanding of JDBC and database internals. Experience with CI/CD pipelines and Distributed and Concurrent Systems Experience with machine learning basics (data pipelines, feature engineering, recall/precision, familiarity with machine learning systems in production) is a huge plus Experience working with relational and non-relational databases ...

payments and banking operations. Job Responsibilities Deliver machine learning and AI solutions for payments and banking operations, from discovery to production rollout Apply agentic engineering practices to build LLM-powered workflows and evaluate their quality, safety, and reliability Contribute to deployment workflows including containerization, CI/CD, automated testing … consumers Partner with product, operations, risk and control, and technology teams to clarify requirements and deliver data-led improvements Build reusable components such as feature engineering pipelines, evaluation harnesses, and orchestration patterns Participate in code and design reviews; contribute to best practices, documentation, and team standards Communicate with ...

shop. Задачи: Design, build and operate machine learning systems for customer engagement, marketing effectiveness, pricing and commercial decision-making Own the end-to-end engineering lifecycle of machine learning products, including data ingestion, feature engineering, deployment, monitoring and optimisation Productionise advanced machine learning solutions and ensure they … operate reliably at ASOS scale Partner with Applied Scientists to turn research and experimentation into scalable production systems Contribute to engineering best practices, operational excellence and capabilities for the MLOps platform Build reusable tooling, frameworks and infrastructure to accelerate machine learning delivery and reduce operational overhead Influence technical direction ...

logistic regression, gradient boosting, random forests, clustering, NLP Proficient in Python and/or R, with strong experience in data quality, model development and feature engineering Strong command of SQL and familiarity with data engineering environments such as Databricks or similar Skilled in data visualisation and storytelling ...

logistic regression, gradient boosting, random forests, clustering, NLP Proficient in Python and/or R, with strong experience in data quality, model development and feature engineering Strong command of SQL and familiarity with data engineering environments such as Databricks or similar Skilled in data visualisation and storytelling ...

cross-functional teams, driving technical deployments, and managing stakeholders. Preferred qualifications Experience in systems design with the ability to explain model architectures, data analysis, feature engineering, and agent design patterns. Experience in business-aligned evaluation design to define metrics, collect data, and measure a program's effectiveness … impact. Experience in process reimagination, assessing legacy workflows, and designing automated, AI-driven alternatives that enhance operational velocity. Experience in context and spec engineering, systematically structuring data, constraints, and operational guidelines to optimize LLM performance. About The Job The Google Cloud Consulting Professional Services team guides customers through ...

cross-functional teams, driving technical deployments, and managing stakeholders. Preferred qualifications: Experience in systems design with the ability to explain model architectures, data analysis, feature engineering, and agent design patterns. Experience in business-aligned evaluation design to define metrics, collect data, and measure a program's effectiveness … impact. Experience in process reimagination, assessing legacy workflows, and designing automated, AI-driven alternatives that enhance operational velocity. Experience in context and spec engineering, systematically structuring data, constraints, and operational guidelines to optimize LLM performance. About the job The Google Cloud Consulting Professional Services team guides customers through ...

business. With rich behavioural, engagement, and service‐quality data at our fingertips, we’re scaling our capabilities and investing in modern Machine Learning Engineering to unlock even more value. Our ML Engineers work alongside data scientists, data engineers and our operations teams to productionise models, automate workflows, and ensure … What you’ll do Build and deploy scalable machine learning solutions that power customer experience, personalisation, and commercial decision‐making across Sky. Develop robust feature engineering pipelines that feed ML models and analytical products. Work closely with data scientists, engineers, and product teams to translate business needs into ...

Lead the development, implementation, and end-to-end delivery of advanced machine learning models and algorithms to drive market operations initiatives. Partner with AI Engineering teams to ensure robust architecture design and deployment patterns for AI/ML models that align with business needs. Mentor and guide a team … managing the machine learning lifecycle. Proficiency in Python and machine learning frameworks such as PyTorch, TensorFlow, and scikit-learn. Strong understanding of data preprocessing, feature engineering, and evaluation techniques. Experience working with large, complex datasets and applying statistical analysis. Familiarity with cloud platforms (AWS, Azure, GCP) and containerization ...

This role is for someone who has demonstrated a track record of delivering high-impact ML projects from concept through production, partnering closely with engineering teams on multi-quarter initiatives that drive measurable business outcomes. Our team builds and optimizes the ML models that power metasearch bidding and auction … your area, including model architecture, system design, data contracts, and integration patterns with existing services Lead multi-quarter ML initiatives in partnership with engineering, product, and business stakeholders, driving projects from ambiguous requirements to production systems at scale Author technical blueprints and system designs that clearly outline objectives, constraints ...

projects end to end, from problem discovery and technical design through to delivery and measuring impact. Collaborate with Product, Compliance, Operations and other engineering teams while maintaining high standards for reliability, testing and code quality. QualificationsWe are fully aware that it is uncommon for a candidate to have … deep understanding of JDBC and database internals. Experience with CI/CD pipelines and Distributed and Concurrent SystemsExperience with machine learning basics (data pipelines, feature engineering, recall/precision, familiarity with machine learning systems in production) is a huge plusExperience working with relational and non-relational databases, query ...

payments and banking operations. Job Responsibilities: Deliver machine learning and AI solutions for payments and banking operations, from discovery to production rollout Apply agentic engineering practices to build LLM-powered workflows and evaluate their quality, safety, and reliability Contribute to deployment workflows including containerization, CI/CD, automated testing … consumers Partner with product, operations, risk and control, and technology teams to clarify requirements and deliver data-led improvements Build reusable components such as feature engineering pipelines, evaluation harnesses, and orchestration patterns Participate in code and design reviews; contribute to best practices, documentation, and team standards Communicate with ...

scalable, secure, and aligned with RELX Responsible AI standards. This is a senior individual‐contributor role with broad influence. You will partner closely with Engineering Leads, Product Managers, Data Scientists, and data teams within LNIP. Key Responsibilities Define and own the AI architecture vision for Protégé in PatentSight+, ensuring … driven enrichment and classification of patent data at scale, ensuring results are dependable, auditable, and consistent with established IP metrics. Establish prompt engineering standards, evaluation harnesses, and quality frameworks to govern LLM behaviour and maintain output accuracy in production. Partner with Product Management to assess technical feasibility and shape ...

evaluate, and improve machine learning models and algorithms for Content AI applications. Design and implement end-to-end machine learning solutions, including data preparation, feature engineering, model training, validation, deployment, monitoring, and continuous optimisation. Apply statistical analysis, experimentation, and data-driven decision-making to assess model performance … reliability, latency, drift, and business impact. Evaluate trade-offs between model quality, scalability, latency, and infrastructure cost when developing AI-powered products. Partner with engineering teams to deploy and scale machine learning systems in production. Produce clear technical documentation and share expertise through mentorship, design reviews, and technical leadership. ...