14 of 14 Remote/Hybrid Feature Engineering Jobs in London

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 ...

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/ ...

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 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 … 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 ...

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. ...

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 ...

data problems and building intelligent tools that scale, you’ll feel right at home. You’ll sit at the intersection of data science, AI engineering, and product innovation. Your work will focus on developing AI‐powered tools that ingest, clean, and transform data from multiple building systems, APIs … detection, forecasting, and optimisation of building performance. Apply modern AI techniques — including LLMs, embeddings, and agentic workflows — to streamline data onboarding, metadata generation, and feature engineering. Create pipelines that turn raw, unstructured, or semi structured data into reliable, production ready datasets. Work closely with engineering teams to deploy ...

Markets domains (counterparties, instruments, legal entities, regulatory taxonomies). Implement graph-RAG patterns for multi-hop reasoning and relationship-aware retrieval. 3. Vector Database Engineering — Architect and optimise the vector storage layer — embedding model selection, chunking strategies (semantic, hierarchical, sliding window), index tuning (HNSW, IVF-PQ), metadata filtering, hybrid … data governance, Delta Lake for reliable storage, Databricks Vector Search for managed embeddings, and Spark/SQL for large-scale data processing and feature engineering. 6. Query Routing & Orchestration — Implement intelligent query routing agents that decompose complex user questions into sub-queries, determine the optimal retrieval source (vector, graph ...

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 ...