The Opportunity
We are looking for an AI Scientist to help build and scale the next generation of AI‑driven healthcare solutions. This role focuses on developing real‑world, secure, and scalable AI/ML applications that support Healthcare Information System (HIS) platforms, moving beyond experimentation into production.
You will work across the AI spectrum, from classical machine learning and deep learning to emerging areas such as generative and agentic AI, helping deliver reliable and impactful tools for clinicians and patients.
Key Responsibilities
Design, train, and fine‑tune ML models using classical statistical methods and modern deep learning approaches.
Support the full lifecycle of model development, from data cleaning and feature engineering through to production deployment.
Implement and evaluate LLM‑based components and agentic workflows to enhance system reasoning and natural language capabilities.
Process and analyse structured and unstructured healthcare data (e.g., FHIR, clinical notes, claims data) to generate meaningful insights.
Conduct rigorous testing to ensure model accuracy, fairness, and safety within regulated healthcare environments.
Optimise models for latency and throughput to support real‑time healthcare applications.
Work independently or collaboratively with scientists and engineers to translate research requirements into production‑ready solutions.
Maintain clear documentation of experiments, model architectures, and data pipelines to support reproducibility and knowledge sharing.
Candidate Profile
Education & Experience
Master’s degree in Computer Science, AI, Machine Learning, or a related field, or a Bachelor’s degree with 5+ years of relevant industry experience.
3+ years of hands‑on experience developing and deploying AI/ML models in a professional or advanced academic environment.
Core Technical Skills
Strong foundation in classical ML techniques (regression, clustering, tree‑based models) and deep learning (neural networks, transformers).
Experience with, or strong interest in, generative AI frameworks, RAG systems, and prompt engineering.
Proficiency in Python and common ML frameworks such as PyTorch, TensorFlow, Scikit‑learn, and Hugging Face.
Nice to Have
Familiarity with Git, Docker, or basic MLOps practices.
Exposure to cloud platforms such as AWS, GCP, or Azure.
Understanding of healthcare data standards and privacy requirements (e.g., HIPAA).
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