7 of 7 Contract Model Validation Jobs in the UK excluding London

Key Responsibilities: Design and implement test plans for AI/ML applications, APIs, and pipelines.Validate machine learning models, ensuring accuracy, fairness, and robustness.Develop automated testing frameworks for data pipelines, model training, and inference.Perform functional, integration, regression, and performance testing on AI systems.Work closely with Data Scientists and AI Engineers to identify and resolve issues. More ❯

Design and implement test plans for AI/ML applications, APIs, and pipelines. Validate machine learning models, ensuring accuracy, fairness, and robustness. Develop automated testing frameworks for data pipelines, model training, and inference. Perform functional, integration, regression, and performance testing on AI systems. Work closely with Data Scientists and AI Engineers to identify and resolve issues. More ❯

per fortnight on site Job Role: Lead rotor dynamic analysis tasks to inform equipment and system design and specification Produce verification evidence to underpin system and equipment performance Undertake model validation including comparison of predicted and test data at system and equipment level Provide direction to more junior engineers and undertake mentoring and coaching Undertake peers' reviews/ More ❯

per fortnight on site Job Role: Lead rotor dynamic analysis tasks to inform equipment and system design and specification Produce verification evidence to underpin system and equipment performance Undertake model validation including comparison of predicted and test data at system and equipment level Provide dire click apply for full job details More ❯

passion for delivering robust, secure, and ethical AI services at scale. Youll be a part of a Scrum team and work closely with developers and architects to design effective validation strategies, automated testing frameworks, and AI-specific evaluation tools - with a builder mindset - rapid prototyping and continuous improvement with agility of a start-up Key Responsibilities: ? Perform functional, performance … to establish service-level quality metrics and observability hooks. ? Validate services against AI regulatory frameworks and ensure traceability, fairness, and robustness in outcomes. ? Participate in threat modelling and security validation of exposed APIs and AI services. ? Provide feedback early in the lifecycle to reduce defects and improve design. ? Mentor junior testers, encourage continuous learning, and contribute to a culture … of innovation. ? AI & Cloud Expertise ? Familiarity with LLM evaluation techniques, output scoring, and validation frameworks. ? Understanding of key concepts such as prompt engineering, RAG , model orchestration, and hallucination detection. ? Experience in testing for accuracy, relevance, and consistency of AI model predictions/generations ? Defining Performance Metrics for AI services and testing for the same ? Awareness of AI More ❯

identify and resolve system-level issues and optimize performance. Document and present results, including technical reports and presentations, to internal and external stakeholders. Support the development of verification and validation strategies for complex systems. Contribute to the continuous improvement of modelling standards, tools, and methodologies. Required Qualifications: Bachelor's or Master's degree in Systems Engineering, Electrical Engineering, Computer … Science, or a related field. Proven experience with MATLAB and Simulink in a professional or academic setting. Strong understanding of control systems, signal processing, and system dynamics. Experience with model-based design (MBD) principles and practices. Ability to interpret and translate system requirements into simulation models. Excellent problem-solving skills and attention to detail. Effective communication and collaboration skills. More ❯

clients. The role has been determined outside IR35 and will run for a minimum of 6-months. Technical Skills Machine Learning & Deep Learning Supervised/unsupervised/reinforcement learning Model selection, training, validation, and evaluation Deep learning architectures (CNNs, RNNs, Transformers) Data Science Statistical analysis, feature engineering, A/B testing Experiment design and hypothesis testing MLOps & Engineering More ❯