ML Ops Engineer
TLDR
Architect end-to-end ML pipelines across Google Cloud and AWS, automate CI/CD, and monitor model performance and data drift.
Requirements:
- Architect, build, and operate end-to-end ML pipelines for training, validation and deployment on Google Cloud and AWS.
- Define, instrument, and maintain logging, monitoring, and alerting for model performance and data drift.
- Automate CI/CD for ML artifacts and infrastructure using GitHub Actions or equivalent.
- Collaborate with cross-functional teams, including frontend engineers, backend engineers, research engineers, and infrastructure engineers.
- Write clean, well-documented, fast, and maintainable code.
- Help ensure our systems have high availability and performance.
- Experience in computer graphics or physics-based simulation.
- Background in setting up Prometheus/Grafana, ELK, or similar monitoring stacks.
- Experience with Vertex AI.
- Experience working with custom Domain-Specific Languages.
About Us:
We are an MIT-born, venture-backed Silicon Valley startup building a real-life 'Jarvis'—an AI Copilot for design and manufacturing. Our goal is to utilize advanced AI, physics simulation, and computer graphics to reduce costs and improve engineering productivity across all steps of the design and manufacturing process.
What we're looking for
Bonus Points
Our tech stack
Benefits
Remote-Friendly
Fully remote role
Foundation EGI is a startup focused on developing an AI Copilot for the design and manufacturing industries. By harnessing advanced AI, physics simulation, and computer graphics, we aim to streamline the entire product development process, enhance engineering productivity, and cut costs.