10 of 10 Remote/Hybrid Feature Engineering Jobs in London

organisations realise the full potential of an AI enabled future. Your responsibilities: Build and ship production ready AI/ML featuresfrom 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 workflowsCI/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/ ...

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 ecosystemsaccelerating … with data engineers, analysts, product managers, and domain experts to translate business requirements into well-defined modelling tasks. Build end to end ML pipelinesfrom 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 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 ...

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 ...

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 ...

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 ...

BI. Secure: Implement rigorous data security, GDPR compliance, and client ring-fencing (Row-Level Security). Innovate: Create SQL and PySpark notebooks to prepare feature engineering datasets for AI/ML models. Collaborate: Work independently via Jira/GitHub, maintaining high CI/CD and documentation standards. What … Bring 5+ years of Data Engineering experience. Expertise: Deep knowledge of Snowflake and Microsoft Fabric . Tech Stack: Advanced SQL , Python , and PySpark (Notebooks). Modelling: Strong relational data modelling and cloud cost optimization skills. DevOps: Experience with GitHub, CI/CD, and workflow orchestration. Randstad Technologies is acting ...

based MMM notebooks to incorporate additional media channels Prepare and validate input data from multiple sources prior to modelling Perform data cleaning, transformation, and feature engineering Run, calibrate, and sense-check MMM model outputs Support model validation and quality checks to ensure outputs are robust and usable Document ...

champion their adoption across teams. Responsibilities •Act as a data strategist to identify, integrate, and leverage new data sources through product capabilities. •Collaborate with engineering teams to build, test, and deploy scalable data products and predictive models. •Execute analytical experiments to address challenges across multiple business domains. •Mine … science or a related field. •Proven expertise in data mining, statistical modelling, and machine learning techniques. •Advanced experience in pattern recognition, predictive modelling, and feature engineering. •Proficiency in Python and SQL, and a solid grasp of software engineering best practices. •Strong understanding of data structures, model evaluation ...

PyTorch, TensorFlow, or scikit-learn. LLM Experience: Proven experience working with large language models (e.g. GPT, Claude, Gemini) in production environments, including prompt engineering, evaluation, and safety considerations. Strong Engineering Skills: Proficiency in Python and TypeScript, with experience building APIs, microservices, and cloud-native applications. Modern AI Tooling … including CI/CD, model versioning, observability, and retraining pipelines. Data Fluency: Skilled in working with structured and unstructured data, including preprocessing and feature engineering. User-Centred Mindset: Ability to translate complex AI capabilities into intuitive and seamless product experiences. Collaborative Approach: Thrives in cross-functional, creative teams ...