Role Summary
Design build and fine-tune AI systems that power real healthcare impact. Success looks like shipping reliable models to production improving accuracy/latency/cost and running reproducible experiments. This role sits within the core AI team and partners with product data and engineering to turn research into scalable products.
Responsibilities
- Design train fine-tune and evaluate models (LLMs embeddings) for production use cases.
- Build reproducible data/ETL pipelines and experiment tracking.
- Implement MLOps best practices (versioning CI/CD for models monitoring rollback).
- Run benchmarking A/B tests error analysis; document findings and decisions.
- Create internal tools/SDKs for inference evaluation and prompt engineering.
- Deploy via REST APIs and optimize for performance and cost.
- Communicate results to technical and non-technical stakeholders.
Qualifications
- Skills: Python (essential); TensorFlow/PyTorch; Hugging Face; Pandas/NumPy/Scikit-learn; ETL; visualization (Matplotlib/Plotly/Tableau/Power BI); NLP & GenAI (LLM fine-tuning embeddings prompt engineering); statistics (hypothesis testing regression).
- Nice to have: C/Java; publications or patents; Unreal Engine exposure.
- Environment: Solid Linux background for development and deployment.
- Education/Experience: Relevant degree or equivalent practical experience.