WHO ARE HEIDI?

Heidi is on a mission to halve the time it takes to deliver world-class care.
We believe that by 2050, every clinician will practice with AI systems that free them from administrative burdens and increase the quality and accessibility of care to patients across the world.
Built for clinicians, by clinicians, at the core of Heidi is its people. We are an eclectic bunch of inventors, builders, scientists, nurses, doctors, mathematicians, designers, creatives, and high-agency executors.
We achieve in 6 months what it takes our competitors 4 years to do. In just 12 months, 20 million patient consults were supported by Heidi, and we’re now powering more than 1 million consults every week.
With our most recent $16.6MM round of funding from leading VC firms, we’re geared up to supercharge our ambitious global growth, starting with the US, Canada, UK and Europe - and we need great people like you to get there.

THE ROLE

Working closely with our Engineering Manager, you’ll be a Senior LLMOps Engineer on the Model Platform team. You are a technical leader responsible for building and scaling the infrastructure that powers our entire model lifecycle.
Your mission is to build a robust, scalable, and reliable platform for deploying and managing our LLMs. You will lead the design and implementation of our LLMOps strategy, ensuring our AI engineers can move models from development to production seamlessly and efficiently.
You will combine your deep infrastructure knowledge with MLOps principles to solve the critical challenges of serving models at scale.

WHAT YOU’LL DO:

• Lead LLMOps Platform Development: Lead the architecture, design, and implementation of our end-to-end LLMOps platform, from data ingestion and model training pipelines to production deployment and monitoring.
• Automate the LLM Lifecycle: Build and maintain robust CI/CD/CT (Continuous Integration/Continuous Delivery/Continuous Training) pipelines to automate the testing, validation, and deployment of large language models.
• Ensure Scalable and Reliable Deployment: Engineer highly available and scalable model serving solutions using modern infrastructure like Kubernetes, ensuring low latency and high throughput for our production services.
• Partner with AI and Engineering Teams: Collaborate closely with AI research and engineering teams to understand their needs, streamline workflows, and create the tooling that accelerates their development cycles.
• Establish MLOps Best Practices: Champion and implement best practices for model versioning, experiment tracking, monitoring, and governance across the organization.
• Mentor and Guide: Mentor mid-level and junior engineers, sharing your deep expertise in infrastructure, automation, and operational excellence to foster a culture of reliability and scalability.