5 of 5 Permanent Feature Extraction Jobs in the South East

optimize machine learning models for predictive analytics, NLP, computer vision, or recommendation systems. Data Pipeline Engineering: Build and maintain robust data pipelines for feature extraction, transformation, and model training. Deployment & Monitoring: Implement models into production environments using MLOps best practices (CI/CD, containerization, monitoring). Performance Optimization … agile teams. Preferred Qualifications Experience with transformer-based models (e.g., BERT, GPT) and generative AI. Knowledge of distributed training and GPU acceleration. Familiarity with feature stores and real-time inference systems. Benefits Competitive salary and bonus structure Flexible working arrangements (hybrid model) Professional development and training budget Health ...

learning models for real-world applications Develop intelligent workflows incorporating feedback loops and agent-based approaches Apply ML techniques such as classification, regression, and feature extraction to complex data sources Implement semantic search, analytics, and root cause analysis functionality Optimise model performance and ensure robustness and scalability Collaborate ...

learning models for real-world applications Develop intelligent workflows incorporating feedback loops and agent-based approaches Apply ML techniques such as classification, regression, and feature extraction to complex data sources Implement semantic search, analytics, and root cause analysis functionality Optimise model performance and ensure robustness and scalability Collaborate ...

high-quality model training Training, fine-tuning, and evaluating AI models including agentic workflows and feedback loops Applying ML/DL techniques (classification, regression, feature extraction) to solve complex wireless testing problems Building semantic search and root-cause analysis capabilities for mobile communication logs Benchmarking and optimising ...

high-quality model training Training, fine-tuning, and evaluating AI models including agentic workflows and feedback loops Applying ML/DL techniques (classification, regression, feature extraction) to solve complex wireless testing problems Building semantic search and root-cause analysis capabilities for mobile communication logs Benchmarking and optimising ...