9 of 9 Remote/Hybrid Feature Engineering Jobs in London

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 ...

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/ ...

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 ...

modern cloud technologies, largescale data systems and cuttingedge tools to support predictive analytics, machine learning initiatives and enterprise reporting. If you're excited by engineering challenge, modern data platforms and solving realworld problems, you'll be a great fit. This is a hybrid role. You will be expected … sessions and department days. Please click here to download the Job description for this role. About you You're an experienced and adaptable data engineering professional who enjoys creating welldesigned, efficient and reliable data solutions. You're comfortable working with complex systems, experimenting with new tools and collaborating with ...

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 ...

data interchange methods. - Knowledge of database environments (e.g., MS SQL, Dataverse, OneLake). - Experience working with or supporting machine learning models, including data preparation, feature engineering and testing. - Understanding of cloud services such as Azure Functions, Logic Apps, Azure AI Foundry or similar. - Strong coding skills, ideally ...

Python, R, SQL, Excel or Power BI. - A good understanding of statistical methods and an interest in machine learning techniques such as regression, feature engineering or natural language processing. - The ability to communicate analytical concepts clearly to nontechnical colleagues. - A collaborative mindset and confidence working with stakeholders across ...

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 ...