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Company Description Diagnuvo Health Ltd. is a real-time clinical intelligence platform specifically designed for emergency departments. The platform integrates patient data such as vitals, lab results, imaging, and clinical inputs to detect early signs of deterioration, predict risks, and guide timely decision-making. By transforming fragmented data into actionable insights, Diagnuvo enhances situational awareness and supports clinicians with dynamic risk scoring, escalation alerts, and evidence-based recommendations. It empowers healthcare professionals to make safer, faster decisions in high-stakes acute care scenarios. Diagnuvo complements clinical judgment by providing precision, foresight, and consistency during critical moments in medicine. Role Description This is an on-site role based in the United Kingdom for the position of AI MLOps Engineer. The AI MLOps Engineer will focus on optimizing AI/ML workflows by designing, deploying, and maintaining scalable and efficient machine learning pipelines. The role involves collaboration with data scientists, engineers, and healthcare professionals to ensure the reliability and scalability of machine learning models in production. Additional responsibilities include automating model deployment, monitoring performance, and troubleshooting any technical issues related to infrastructure and data. Qualifications Experience with building, deploying, and managing machine learning pipelines on cloud platforms.Proficiency in programming languages such as Python and familiarity with ML frameworks like TensorFlow or PyTorch.Experience with containerization tools such as Docker and orchestration tools like Kubernetes.Strong understanding of CI/CD pipelines, version control systems, and automation tools.Familiarity with monitoring and troubleshooting machine learning models and infrastructure in production environments.Strong problem-solving skills and the ability to work in cross-functional teams.Preferred qualifications include a degree in Computer Science, AI, or a related field, and familiarity with healthcare-related datasets and workflows.
