As a Principal AI Scientist, you will lead the technical vision for integrating sophisticated
AI into high-stakes Healthcare Information Systems (HIS). We are looking for a seasoned
expert who prioritizes reliability, security, and scalability over hype.
You will design and deploy end to end AI/ML applications that solve real world clinical and
operational challenges. While you will leverage Generative and Agentic AI to push
boundaries, your foundation must be rooted in rigorous Classical ML, Deep Learning, and
Data Science to ensure our solutions are production grade and mission critical.
Key Responsibilities
1. End to End System Architecture
• Design & Deploy: Lead the full lifecycle of AI applications, from initial data
exploration and model training to deploying secure, scalable services in a
production environment.
• Hybrid Modeling: Intelligently combine Classical ML (Random Forests, XGBoost,
etc.) with Deep Learning and Generative AI to solve complex healthcare problems
efficiently.
• Agentic Frameworks: Architect agent based reasoning systems to automate
clinical workflows while maintaining strict human in the loop safety standards.
2. Technical Leadership & Strategy
• Technical Authority: Serve as the lead architect for AI/ML strategy, ensuring all
models are explainable, robust, and compliant with healthcare regulations
(HIPAA/HITRUST).
• Research to Reality: Bridge the gap between theoretical research and practical
engineering, ensuring prototypes are built with deployment and maintenance in
mind.
• Mentorship: Elevate the engineering team through rigorous code reviews,
architectural design docs, and best practices in data science.
3. Data & Operational Excellence
• Data Integrity: Oversee the processing of structured and unstructured healthcare
data (FHIR, HL7, clinical notes) ensuring high data quality and security.
• Validation: Implement rigorous model evaluation frameworks to detect bias and
ensure clinical accuracy.
Candidate Profile
Education & Experience:
• PhD + 5 years OR Master's + 8 years in Computer Science, AI, Machine Learning, or
a related quantitative field.
• Proven Track Record: Extensive experience designing and deploying real world,
large-scale AI applications (not just research papers or POCs).
Core Technical Stack:
• Foundational ML: Expert knowledge of Classical ML (statistical modeling,
ensemble methods) and Deep Learning (CNNs, RNNs, Transformers).
• AI Innovation: Strong experience with Generative AI and Agentic frameworks (e.g.,
LangChain, LangGraph) as part of a broader solution stack.
• Programming: Expert level Python and proficiency in libraries such as PyTorch,
TensorFlow, Scikit learn, and Hugging Face.
The Principal Edge (Preferred):
• MLOps & Engineering: Experience with MLE/MLOps practices (CI/CD for ML,
model monitoring, versioning).
• Cloud Infrastructure: Familiarity with Cloud Engineering (AWS, GCP, Azure) and
containerization (Docker/Kubernetes).
• Domain Expertise: Prior experience in Healthcare, Life Sciences, or similarly
regulated environments