We're seeking a Head of Engineering to lead our system development, manage our engineering team, and streamline our operations.Core Responsibilities:- Lead and mentor a team of ML/AI engineers in developing and deploying production-grade medical imaging solutions.- Orchestrate the end-to-end ML lifecycle, from model development to production deployment using Nvidia Triton Servers.- Design and implement scalable MLOps infrastructure to support model training, validation, and monitoring.- Establish and maintain CI/CD pipelines for reliable model deployment and updates.- Collaborate with cross-functional teams to integrate AI solutions into our medical device products.- Drive technical decisions while ensuring compliance with FDA regulations and medical device standards.- Partner with product and clinical teams to translate cardiologist needs into technical requirements.Technical Leadership:- Architect and oversee development of RESTful APIs for model serving and integration.- Lead infrastructure decisions for cloud-based deployment using GKE and Terraform.- Implement monitoring and observability solutions using Sentry for production model performance.- Guide the team in building visualization tools for model outputs, as well as evaluations measuring the efficacy of existing models on new datasets.- Establish best practices for version control, code review, and documentation.Required Experience:- Strong background in Python, with experience in Django and REST frameworks.- Proven track record of leading technical teams and managing complex projects.- Experience with containerization (Docker) and orchestration (Kubernetes).- Understanding of ML model serving architectures and MLOps best practices.Preferred Qualifications:- Experience with medical imaging or healthcare applications.- Familiarity with FDA requirements for medical devices.- Background in React and modern frontend development.- Experience with ClearML or similar ML experiment tracking platforms.- Knowledge of cloud infrastructure management using Terraform.Tech Stack Proficiency:- Infrastructure: GKE, Helm, Terraform, ECR- Backend: Django, Python, Nvidia Triton Servers- Frontend: React- EMLOps: ClearML, GitHub Actions- Monitoring: SentryWhat Success Looks Like:- Within 3 months: Fully understand our ML pipeline and deployment architecture.- Within 6 months: Lead significant improvements to our model deployment process.- Within 1 year: Successfully shepherd new AI features through FDA approval process.