Hybrid Applied AI Engineer – Machine Learning, NLP

at Grupo Med4U

• Job title: Applied AI Engineer – Machine Learning, NLP
• Job type: Full Time
• Experience level: Mid level, Senior
• Salary: Not specified
• Degree requirement: Bachelor's Degree
• Tech skills: Python, PyTorch, Tensorflow
• Location requirements: Hybrid, Curitiba, Brazil

About the role

• Applied AI Engineer at Spesia transforming clinical data into real healthcare AI tools for doctors and patients. Developing machine learning models and implementing AI in health applications.

Responsibilities

• Develop and implement Artificial Intelligence solutions applied to healthcare, transforming clinical data into digital products that deliver real value to doctors, patients, and care teams.
• Develop, test and deploy machine learning and NLP models in real-world environments using clinical and biomedical data;
• Build and maintain data and inference pipelines, handling structured and unstructured data (primarily clinical text);
• Integrate AI models into digital products and clinical decision support systems;
• Develop APIs, services and processing routines to make models available in production;
• Work with real-world EHR data, performing cleaning, normalization, structuring and enrichment;
• Explore and adapt Transformer-based models (e.g., BERT, Llama, among others), with a focus on practical application;
• Evaluate models with metrics appropriate to the clinical context and iterate based on user feedback;
• Collaborate with physicians, engineers and product teams to define and evolve solutions;
• Translate models and analyses into product-applicable features;
• Contribute to technical documentation of developed solutions.

Requirements

• Degree in Computer Science, Data Science, Biomedical Informatics or related fields;
• Hands-on experience developing machine learning or NLP solutions;
• Proficiency in Python;
• Experience with libraries such as PyTorch, TensorFlow, HuggingFace or similar;
• Experience with collaborative development (Git, code review, pull requests);
• Experience building applications (beyond notebooks);
• Familiarity with model deployment, APIs or data pipelines;
• Knowledge of NLP, information extraction and use of LLMs;
• Good communication skills for working with multidisciplinary teams.