Ylempi AI-kehittäjä - Thermo Fisher Scientific Oy - Etätyö - Globaali

Join our AI team where you'll design and implement artificial intelligence solutions that help make the world healthier, cleaner, and safer. As a Staff Engineer in Artificial Intelligence, you'll architect and develop advanced AI/ML solutions that drive innovation across our diverse business units. You'll collaborate with data scientists, researchers, and business stakeholders to create scalable AI architectures, optimize model performance, and implement state-of-the-art machine learning solutions. This role includes developing predictive models for patient and donor risk, working with healthcare datasets, and integrating AI capabilities into applications, often leveraging cloud-based platforms. Design, develop, and deploy AI/ML models and algorithms. Build predictive models for patient and donor risk assessment (e.g., eligibility, adverse outcomes, fraud/risk scoring). Work with structured and unstructured healthcare/life sciences data (EHR, clinical, donor data). Perform data preprocessing, feature engineering, and model evaluation. Integrate AI models into production systems via APIs or microservices. Collaborate with data scientists, clinicians, and business stakeholders. Optimize model performance, accuracy, and interpretability. Utilize cloud AI/ML platforms (AWS, Azure, Google Cloud). Monitor, retrain, and maintain models in production (MLOps practices). Ensure compliance with healthcare regulations (HIPAA, FDA, GDPR as applicable). Apply ethical AI practices, including bias detection and model transparency. Stay updated with advancements in AI, healthcare analytics, and risk modeling. Required qualifications include a Bachelor’s or Master’s degree in computer science, AI, Data Science, or related field, 5+ years of experience in AI/ML development, strong programming skills in Python (preferred), C#.Net, Java, or C++, experience with frameworks like TensorFlow, PyTorch, Scikit-learn, and experience developing risk models (classification, regression, survival analysis). Familiarity with NLP frameworks, healthcare or life sciences data, data preprocessing (Pandas, NumPy), REST APIs, version control (GitHub), problem-solving, and communication skills. Preferred qualifications include experience with Generative AI, LLMs, healthcare data standards (HL7, FHIR), cloud AI services (SageMaker, Azure ML, Vertex AI), MLOps tools (MLflow, Kubeflow, Docker, Kubernetes), big data tools (Spark, Kafka), clinical analytics, epidemiology, biostatistics, and AI or cloud certifications.