The Opportunity
As a Principal AI Scientist, you will lead the technical vision for integrating advanced AI capabilities into high-stakes Healthcare Information Systems (HIS). This role requires a senior expert who values reliability, security, and scalability over hype.
You will design and deploy end-to-end AI and machine learning applications that address real-world clinical and operational challenges. While leveraging Generative and Agentic AI, the role is grounded in strong expertise in Classical ML, Deep Learning, and Data Science to ensure production-grade, mission-critical solutions.
Key Responsibilities
1. End-to-End System Architecture
- Design and Deploy: Lead the full lifecycle of AI applications, from data exploration and model training through to deployment of secure, scalable production services.
- Hybrid Modeling: Combine Classical ML (e.g. Random Forests, XGBoost) with Deep Learning and Generative AI to solve complex healthcare problems effectively.
- Agentic Frameworks: Architect agent-based reasoning systems to automate clinical workflows while maintaining strict human-in-the-loop safety standards.
2. Technical Leadership and Strategy
- Technical Authority: Act as lead architect for AI and ML strategy, ensuring models are explainable, robust, and compliant with healthcare regulations (HIPAA, HITRUST).
- Research to Reality: Translate research concepts into practical engineering solutions built with deployment and maintainability in mind.
- Mentorship: Support and elevate engineering teams through code reviews, architectural design guidance, and best practices in data science and machine learning.
3. Data and Operational Excellence
- Data Integrity: Oversee processing of structured and unstructured healthcare data (FHIR, HL7, clinical notes), ensuring strong data quality and security standards.
- Validation: Implement rigorous model evaluation frameworks to detect bias and maintain clinical accuracy.
Candidate Profile
Education and Experience
- PhD plus 5 years OR Master’s plus 8 years of experience in Computer Science, Artificial Intelligence, Machine Learning, or a related quantitative discipline.
- Proven track record designing and deploying large-scale, real-world AI applications beyond research prototypes or proofs of concept.
Core Technical Stack
- Foundational ML: Expert knowledge of Classical ML (statistical modelling, ensemble methods) and Deep Learning (CNNs, RNNs, Transformers).
- AI Innovation: Strong experience using Generative AI and Agentic frameworks (e.g. LangChain, LangGraph) as part of broader solution architectures.
- Programming: Expert-level Python with proficiency in libraries such as PyTorch, TensorFlow, Scikit-learn, and Hugging Face.
Preferred Experience
- MLOps and Engineering: Experience with MLE/MLOps practices including CI/CD for ML, model monitoring, and version control.
- Cloud Infrastructure: Familiarity with cloud platforms (AWS, GCP, Azure) and containerisation technologies such as Docker and Kubernetes.
- Domain Expertise: Experience working in Healthcare, Life Sciences, or other regulated industries.
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