Machine Learning Engineer
Investigo
Reference: BH-
Apply fast, check the full description by scrolling below to find out the full requirements for this role.
Machine Leaning Engineer 735 per day – Inside IR35 London / 2 days a week on site 2 years My client is an instantly recognisable consultancy who require a Machine Learning Engineer for a business-critical programme.
Key Responsibilities: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.Production Deployment: Deploy and supervise ML models in production environments, ensuring high availability, optimal performance, and continued relevance.Experimentation: Lead the design and analysis of A/B tests and offline experiments to evaluate model efficacy and support continuous improvement.Cross-Functional Collaboration: Engage with multidisciplinary teams to align machine learning initiatives with business objectives and user needs. xrnqpay Research & Innovation: Evaluate emerging research in machine learning, deep learning, and personalisation for potential integration within existing systems.
Apply fast, check the full description by scrolling below to find out the full requirements for this role.
Machine Leaning Engineer 735 per day – Inside IR35 London / 2 days a week on site 2 years My client is an instantly recognisable consultancy who require a Machine Learning Engineer for a business-critical programme.
Key Responsibilities: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.Production Deployment: Deploy and supervise ML models in production environments, ensuring high availability, optimal performance, and continued relevance.Experimentation: Lead the design and analysis of A/B tests and offline experiments to evaluate model efficacy and support continuous improvement.Cross-Functional Collaboration: Engage with multidisciplinary teams to align machine learning initiatives with business objectives and user needs. xrnqpay Research & Innovation: Evaluate emerging research in machine learning, deep learning, and personalisation for potential integration within existing systems.
Reference: WJ-799_34424610