ML Ops Engineer

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 BS in Computer Science or a related field. 5+ years of experience as a AI/ML Ops, DevOps, Infrastructure Engineer or equivalent. Expert-level Python and TypeScripts skills. Experience with Docker, Kubernetes, Terraform, Google Cloud and AWS. Deep understanding of machine learning models, including LLMs. Experience designing and maintaining CI/CD pipelines to fine-tune or train ML models. Excellent written and verbal communication skills. Bonus Points 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. Our tech stack Google Cloud, AWS Python, TypeScript Protobuf, gRPC Next.JS, React.JS GitHub Actions Docker, Kubernetes, Spinnaker PostgreSQL $120,000 - $160,000 a year We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us. Foundation EGI