AI Engineer for International Health Group

The Barcelona Institute for Global Health (ISGlobal) is a cutting-edge institute addressing global public health challenges through research, translation into policy and education. ISGlobal has a broad portfolio in communicable and non-communicable diseases including environmental and climate determinants, and applies a multidisciplinary scientific approach ranging from the molecular to the population level. Research is organized in five programs: Climate, Air Pollution, Nature and Urban Health; Environment and Health over the Lifecourse; Global Viral and Bacterial Infections; Malaria and Neglected Parasitic Diseases and Maternal Child and Reproductive Health. ISGlobal is accredited with the Severo Ochoa distinction, a seal of excellence of the Spanish Science Ministry.

WHAT WE ARE LOOKING FOR

ISGlobal is looking for a proactive AI Engineer with experience in Large Language Models (LLMs) to design, fine-tune, deploy, and optimize generative AI systems across multiple projects within the International Health Service at Hospital Clínic de Barcelona (https://www.clinicbarcelona.org/en/service/international-health). You will contribute to the design, fine-tuning, deployment, and evaluation of large language models and generative AI tools for use in global health research, clinical workflows, and digital epidemiology. Working closely with multidisciplinary teams, you will ensure methodological rigor, timely deliverables and data-driven insights that advance our global-health objectives. 

KEY RESPONSIBILITIES

• Develop and Integrate AI Solutions: Design, fine-tune, and deploy LLMs and generative AI systems specialized in travel medicine, supporting clinical decision-making, risk assessment, and health guidance for international travelers.
• AI Assistant Development: Build and maintain intelligent assistants and conversational agents for the travel medicine app, ensuring accurate, safe, and context-aware medical guidance aligned with validated clinical protocols.
• Knowledge Engineering: Curate, structure, and maintain high-quality medical and epidemiological datasets to enhance model training, retrieval-augmented generation (RAG) pipelines, and domain-specific reasoning.
• Model Evaluation and Optimization: Continuously assess AI system performance through benchmarking, human evaluation, and real-world feedback, improving accuracy, reliability, and user trust.
• Responsible AI Practices: Ensure compliance with ethical, privacy, and safety principles in AI development, including GDPR alignment, bias monitoring, and transparent documentation of AI outputs and limitations.
• Documentation and Reproducibility: Maintain technical documentation, model cards, and SOPs for AI workflows.

SPECIFIC DUTIES

• Model Design and Implementation: Develop domain-specific LLMs using modern frameworks (e.g., PyTorch, Hugging Face, LangChain), integrating structured medical knowledge and multilingual capabilities. AI Assistant Integration: Collaborate with mobile and backend developers to embed AI functionalities (chatbots, triage tools, symptom interpreters, or travel-health guides) into the app ecosystem. Data Preparation and Curation: Build pipelines to clean, harmonize, and enrich health, mobility, and climate datasets for use in AI training and evaluation. Continuous Improvement: Experiment with fine-tuning, prompt engineering, RAG pipelines, and memory systems to enhance context retention and user personalization. Monitoring and Evaluation: Implement dashboards and analytics tools to track AI usage, performance, and safety metrics in real time.

AUXILIAR TASKS

This job description reflects the present requirements of the post but may evolve at any time in the future as duties and responsibilities change and/or develop providing there is appropriate consultation with the post-holder.

This job description is not a definitive or exhaustive list of responsibilities but identifies the key responsibilities and tasks of the post holder. The specific objectives of the post holder will be subject to review as part of the individual professional assessment process.

SKILLS

• AI and NLP Expertise: Experience in training, fine-tuning, and deploying LLMs or generative AI systems using modern architectures and frameworks. Programming Proficiency: Expert-level skills in Python (PyTorch, transformers, LangChain), and familiarity with cloud infrastructure (GCP, AWS, or Azure).
• Experience with data pipelines and SQL.
• Ability to explain AI concepts clearly to technical and non-technical stakeholders, and to work within multidisciplinary, international teams.
• Domain Knowledge: Familiarity with global health, e