AI Engineer
Role details
Job location
Tech stack
Job description
Job Title: AI Engineer
Location: West London, UK (Hybrid Working)
Type: Contract (Inside IR35)
Duration: 12 Months
Brief:
The team is committed to advancing AI-driven innovations in two key areas: developing AI-powered predictive healthcare solutions that integrate real-world health data from wearables, IoT devices, and medical records to enhance early disease detection and enable personalized health monitoring, and creating AI-powered accessibility solutions that improve digital and physical accessibility for individuals with disabilities, with a particular focus on vision and cognitive support. The role within the team involves contributing to the development, implementation, and optimization of these AI solutions to ensure they meet user needs and drive meaningful impact in both healthcare and accessibility domains.
Responsibilities:
-Develop and optimize machine learning models for disease prediction, early diagnosis and personalised healthcare solutions. -Process and analyze structured and unstructured health data (EHR, Wearables, HL7/FHIR) and implement deep learning algorithms for predictive healthcare applications. -Contribute to research on AI-driven personalization strategies to empower users in managing their health effectively. -Develop AI-powered accessibility solutions for our products, leveraging multi-modal AI (text, image, audio). -Adhere to data privacy regulations (GDPR, MDR, HIPPA, EHDS) and implement ethical AI practices. Requirements:
-A PhD in the related field with a good level of work experience, or a Master's Degree with extensive work experience in the related field. -Good understanding of cardiovascular diseases, risk factors and clinical workflows. -Expertise in handling missing data, outliers and feature engineering. -Experience in processing Health data from various sources (EHR, Wearables) and formats (e.g., HL7/FHIR, CSV, PDF, PNG, JPEG). -Skilled in analysing time-series data from wearable sensors or medical devices (e.g., ECG, PPG). -Proficient in hypothesis formulation, testing and correlation analysis using Python. -Experience with supervised/unsupervised learning and model interpretability (e.g., SHAP, LIME). -Skilled in creating clear visualizations with Matplotlib or Seaborn. -Expertise in metrics like AUC-ROC, precision, recall, and cross-validation techniques. -Experience deploying predictive models in clinical settings or EHR systems. Desirables:
-Familiarity with healthcare regulatory frameworks (GDPR, MDR, HIPAA, EHDS). -Ability to work with diverse teams and communicate technical insights effectively. -Knowledge of Deep Learning, Reinforcement Learning and Federated Learning architectures. -Familiarity with cloud platforms (AWS, GCP, Azure) and big data tools.
Requirements
A PhD in the related field with a good level of work experience, or a Master's Degree with extensive work experience in the related field. -Good understanding of cardiovascular diseases, risk factors and clinical workflows. -Expertise in handling missing data, outliers and feature engineering. -Experience in processing Health data from various sources (EHR, Wearables) and formats (e.g., HL7/FHIR, CSV, PDF, PNG, JPEG). -Skilled in analysing time-series data from wearable sensors or medical devices (e.g., ECG, PPG). -Proficient in hypothesis formulation, testing and correlation analysis using Python. -Experience with supervised/unsupervised learning and model interpretability (e.g., SHAP, LIME). -Skilled in creating clear visualizations with Matplotlib or Seaborn. -Expertise in metrics like AUC-ROC, precision, recall, and cross-validation techniques. -Experience deploying predictive models in clinical settings or EHR systems. Desirables:
-Familiarity with healthcare regulatory frameworks (GDPR, MDR, HIPAA, EHDS). -Ability to work with diverse teams and communicate technical insights effectively. -Knowledge of Deep Learning, Reinforcement Learning and Federated Learning architectures. -Familiarity with cloud platforms (AWS, GCP, Azure) and big data tools.
