11 of 11 Remote/Hybrid Feature Engineering Jobs in England

critical role in designing, building, and maintaining data pipelines and infrastructure that enable the development and deployment of machine learning models and drive engineering excellence. You will collaborate closely with data scientists, and lead ML engineers, and software engineers to ensure data is clean, accessible, and optimised for large … systems (e.g., PySpark, Hadoop). Data Preparation: Collaborate with data scientists to prepare and transform raw data into formats suitable for machine learning, including feature engineering and data augmentation. Automation: Implement automation tools and frameworks (CI/CD) to streamline the deployment and monitoring of machine learning models ...

Partner closely with data scientists to turn experimental models into well-engineered, production-ready systems Develop and maintain end-to-end ML pipelines, including feature engineering, training, evaluation, versioning, monitoring, and automated retraining Contribute to MLOps capabilities, including CI/CD pipelines, model registries, infrastructure automation, and containerised … regulatory requirements Advise business and product teams on opportunities for automation, predictive modelling, and GenAI enablement Contribute to reusable ML components, internal frameworks, and engineering best practices Stay up to date with emerging AI trends, tooling, and foundation models to inform strategic decision-making About You You will ...

other AI applications. Experiment with algorithms and optimize hyperparameters for performance. Data Preparation Collect, clean, and preprocess large datasets for training and validation. Implement feature engineering and data augmentation techniques. Integration & Deployment Develop APIs or services to integrate ML models into production systems. Work with MLOps tools … practices. Implement bias detection and mitigation strategies. Your Profile Essential Skills/Knowledge/Experience (Up to 10) 10+ years of experience in software engineering, with 5+ years in AI/ML. Expert in Python, PyTorch, TensorFlow, and NLP frameworks. Proven experience with LLMs, transformer architectures, and LangChain/ ...

services (SageMaker, Lambda) Familiarity with containerisation (Docker) and orchestration (Kubernetes/ECS) Knowledge of LLMOps and GenAI tools (LangChain, LangSmith) Understanding of feature engineering and vector databases Strong grasp of CI/CD practices for ML deployment Why Join Us: Work with niche datasets and cutting-edge tech ...

Collaborate with data engineers, analysts, and product teams to translate business needs into AI-driven solutions Contribute to the development of data pipelines and feature engineering workflows Integrate models into production using APIs, batch jobs, or real-time systems Apply best practices around experimentation, evaluation, versioning, and monitoring ...

cloud and on-prem data platforms , automation, and emerging AI-driven capabilities, and is specifically suited to candidates with strong, hands-on data engineering experience , particularly in building, operating, and optimising complex data pipelines, data models, and integration workflows at scale. Role Overview and Responsibilities As a Data & Automation … delivery processes using technologies such as Azure Data Factory, Databricks, SSIS, and SQL . Reducing manual interventions through automation and standardisation , including data preparation, feature engineering, and training-data pipelines. Preparing data models and datasets (DWH/Lakehouse) to support business intelligence, analytics, and operational reporting. Monitoring ...

things that go into production, and wants to learn from experienced engineers in a fast-moving environment. You’ll be part of a modern engineering function working on large-scale data and AI products, contributing to model development, data pipelines, and deployment workflows that power mission-critical platforms. … Build, train, and evaluate machine learning models using Python 📊 Work with large, complex datasets to support AI-driven products and insights 🧠 Assist with feature engineering, model experimentation, and performance optimisation ☁️ Contribute to data and ML pipelines in cloud-based environments 🗄 Work with structured and semi-structured data using ...

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 ...

optimisation project. You will design and implement predictive and prescriptive models for Maintenance, Repair, Overhaul AI Solutions. Performing exploratory data analysis and feature engineering. Collaborate with Data Engineers to ensure data readiness for modelling. Communicate findings and recommendations to business stakeholders. Continuously improve models based on feedback and operational ...

compute performance. Deploy pipelines using CI/CD and IaC. Deliver curated datasets and semantic layers for BI, analytics, and ML. Support ML feature engineering and model delivery. Define scalable data architecture patterns and integration designs (ingestion, storage, transformation, analytics). Develop and maintain conceptual, logical, and physical … support scalability and strategic needs. Personal Requirements: 3–5+ years’ experience with Microsoft SQL Server (2016+) and SSIS in production environments. Strong SQL Server engineering: advanced T-SQL, indexing, performance tuning, execution plans, statistics, partitioning, tempdb optimisation. Proven experience with ETL/ELT development and design patterns (incremental loads ...

proficiency and familiarity with popular libraries Knowledge of frameworks for building complex ML models (e.g. TensorFlow, pytorch or keras) Ability to pre-process and feature engineer data (cleaning, transformation, feature extraction) Ability to deploy and serve models in prod ready environments (requiring knowledge of containerisation, orchestration, and model ...