Senior Software Development Engineer (DevOps) - Video Insights
Core
Design and maintain scalable cloud infrastructure and MLOps pipelines for deploying video AI models and services at massive scale.
Role type
Senior DevOps Engineer (Infrastructure & MLOps)
Builds
Kubernetes clusters, containerized deployment pipelines, and observability systems for GPU-accelerated video AI workloads
Domain
Cloud Infrastructure, Distributed Systems, Video AI
Deliverable
infrastructure
Required skills
Kubernetes, container orchestration, cloud platform engineering, Python/Go programming, distributed systems, GPU infrastructure, ML infrastructure tools, infrastructure-as-code, CI/CD pipelines, observability
Preferred skills
CUDA, database optimization for large-scale workloads
Technologies
Docker, Kubernetes, AWS/GCP/Azure, Python, Go, Terraform, CloudFormation, Helm, Kubeflow, MLflow, Ray, Prometheus, ELK, Datadog
Responsibilities
Design and implement robust, scalable cloud infrastructure for deploying video AI models; Build and maintain Kubernetes clusters and containerized deployment pipelines optimized for GPU workloads; Establish monitoring, logging, and observability systems for AI/ML production services; Develop infrastructure-as-code practices and automation for rapid, reliable deployment; Optimize cloud costs while maintaining performance and reliability standards; Work with ML engineers to develop efficient MLOps pipelines for model training, evaluation, and serving; Establish and maintain security, compliance, and data governance practices for AI systems; Mentor engineering teams on infrastructure best practices and cloud architecture patterns; Drive incident response, postmortems, and continuous improvement of system reliability
Seniority
Senior, hands-on IC