ML Ops Engineer

Role Description

• 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.

Qualifications

• BS in Computer Science or a related field.
• 5+ years of experience as an AI/ML Ops, DevOps, Infrastructure Engineer or equivalent.
• Expert-level Python and TypeScript 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.

Requirements

• 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.

Benefits

• We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses.
• 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.