Torch Dental is looking to improve the lives of healthcare providers by simplifying supply ordering, providing transparent pricing, and modernizing a previously outdated ordering process. The company aims to streamline the procurement process and secure the best prices for products for office-based healthcare supply industry, starting with dental practices, by enabling LLM-powered features.
Requirements
- Strong experience with cloud platforms (AWS, GCP, Azure) and deploying secure, compliant workloads
- Hands-on experience with containerization and orchestration (Docker, Kubernetes)
- Experience designing and managing infrastructure as code to provision, maintain, and scale cloud resources reliably and reproducibly (e.g., Terraform, Pulumi, or similar tools)
- Proven experience building production-grade AI/ML infrastructure, including LLM inference pipelines
- Familiarity with HIPAA or other regulated healthcare environments
- Demonstrated record of building scalable, secure, and reliable systems
- 5+ years of experience in software engineering or infrastructure engineering
Responsibilities
- Own the end-to-end AI infrastructure architecture, including cloud deployment, container orchestration, and pipelines
- Build and maintain HIPAA-compliant systems for sensitive healthcare data
- Lead decisions around security, compliance, cost, performance, and scaling of AI workloads
- Implement robust monitoring, alerting, and observability systems for AI pipelines and production models
- Collaborate cross-functionally with engineering, product, and operations teams to integrate AI capabilities into Torch’s platform
- Mentor and guide other engineers on best practices in AI infrastructure, DevOps, and compliance
- contributing to the backend services that support them
Other
- Comfortable collaborating with non-engineers, including product managers, designers, and business stakeholders
- Experience leading AI infrastructure projects end-to-end in a startup environment
- Contributed to architecture or coding standards for engineering teams
- Experience optimizing AI workloads for performance, cost, or latency
- Experience in early-stage product ideation and design for AI-enabled feature
