The Impact You Will Create

• Build & Maintain ML Infrastructure: Design, implement, and maintain our cloud-based infrastructure for large-scale computer vision model training and data management.
• Automate ML Pipelines: Engineer and deploy automated, production-grade ML pipelines for seamless data processing, model training, validation, and deployment.
• Enable AI/ML Teams: Collaborate directly with Data Scientists and AI Engineers to streamline and accelerate the entire model development lifecycle.
• Ensure Scalability & Reliability: Architect and operate robust, secure, and efficient infrastructure for our large-scale AI solutions.

Experience And Skills You Will Need

• Production MLOps Experience: Strong, relevant work experience operating and scaling machine learning systems and AI workflows in a production environment.
• Kubernetes Mastery: Deep, hands-on proficiency with Kubernetes for scheduling and scaling ML training jobs and complex workloads.
• ML Pipeline Expertise: Proven ability to build, manage, and troubleshoot ML pipelines and serving infrastructure. Direct experience with Argo Workflows and ArgoCD is an advantage.
• MLOps Tooling: Proficiency with modern MLOps tools, especially MLFlow for experiment tracking and model management.
• Infrastructure as Code (IaC): Solid practical experience managing cloud infrastructure using Terraform.
• Pragmatic Problem-Solver: Demonstrated ability to quickly and independently solve complex technical challenges with reliable, scalable solutions.