Mozn

Infrastructure Engineer

• AI Platform Engineering: Design, deploy, and operate enterprise AI/ML platforms.
• Build self-service platforms for Data Scientists and ML Engineers.
• Deploy and operate Kubeflow, MLflow, KServe, Ray, or similar AI platforms.
• Design infrastructure supporting model training, experimentation, feature engineering, and inference.
• Build highly available and scalable model serving infrastructure.
• GPU Infrastructure: Design and operate GPU clusters for large-scale AI workloads.
• Optimize GPU scheduling, utilization, sharing, autoscaling, and resource allocation.
• Deploy and manage NVIDIA GPU Operator and GPU-enabled Kubernetes environments.
• Optimize distributed GPU training performance across multi-node clusters.
• Troubleshoot AI infrastructure performance bottlenecks.
• MLOps & Platform Automation: Build CI/CD pipelines for ML workloads.
• Automate AI infrastructure provisioning using Infrastructure as Code.
• Implement monitoring and observability for GPU utilization, model serving, training jobs, and inference latency.
• Collaborate closely with Data Science teams to improve platform usability, performance, and reliability.

AI Infrastructure Engineer III

DevOps Engineer

• Own reliability, performance, scalability, and operational health of MySQL, PostgreSQL, MongoDB, Elasticsearch/OpenSearch, Kafka, StarRocks, ClickHouse, and similar platforms
• Define best practices for how product engineering teams use transactional, document, search, messaging, and analytical platforms
• Design and maintain highly available, scalable, and resilient platform services, including replication, backup, recovery, failover, and disaster recovery capabilities
• Perform capacity planning, performance tuning, workload reviews, upgrades, patching, and lifecycle management for platform services
• Identify and resolve risks such as slow queries, hot partitions, consumer lag, replication lag, index growth, retention issues, and storage saturation
• Troubleshoot and resolve complex production issues related to databases, messaging systems, search platforms, and distributed data platforms
• Enable and support Kubernetes-based deployments of database, messaging, search, and analytical platforms using cloud-native patterns and operational best practices
• Use automation, Infrastructure as Code, GitOps, and CI/CD to make backing services repeatable, reliable, and easier to operate
• Collaborate with Product Engineering, SRE, Security, Data Engineering, and Cloud Platform teams to improve reliability, performance, availability, and security posture

AI Infrastructure Engineer III

Cloud Platform Engineer – Data Reliability, Backing Services