AI Infrastructure Engineer
About The Position
Bright Vision Technologies is a forward-thinking software development company dedicated to building innovative solutions that help businesses automate and optimize their operations. We leverage cutting-edge cloud data platform technologies to design scalable, secure, and high-performance analytics environments. As we continue to grow, we’re looking for a skilled AI Infrastructure Engineer to join our dynamic team and contribute to our mission of transforming business processes through technology. This is a fantastic opportunity to join an established and well-respected organization offering tremendous career growth potential.
Requirements
- Design and manage AI/ML Infrastructure optimized for GPU Computing using NVIDIA CUDA, enabling high-throughput training and inference workloads.
- Develop and automate scalable environments with Python scripting on Linux, leveraging Docker for containerization and Kubernetes for orchestration.
- Deploy and optimize AI workloads across Cloud Platforms (AWS, Azure, GCP), configuring GPU clusters for cost-effective scaling.
- Implement AI Workload Orchestration tools to schedule, manage, and monitor distributed training jobs across multi-node setups.
- Build High-Performance Computing (HPC) systems with Distributed Systems expertise, focusing on low-latency Storage & Networking for AI (e.g., NVMe, InfiniBand).
- Provision infrastructure using Infrastructure as Code (Terraform), ensuring reproducible and version-controlled deployments.
- Establish CI/CD pipelines with Git integration for automated building, testing, and rollout of AI infrastructure components.
- Set up Monitoring & Observability stacks (e.g., Prometheus, Grafana) to track GPU utilization, cluster health, and performance bottlenecks.
- Collaborate in Agile methodologies, delivering iterative improvements to AI infrastructure through sprints and cross-functional teamwork.
- Optimize resource allocation for AI pipelines, reducing costs while maximizing throughput for large-scale model training and serving.
Responsibilities
- Design and manage AI/ML Infrastructure optimized for GPU Computing using NVIDIA CUDA, enabling high-throughput training and inference workloads.
- Develop and automate scalable environments with Python scripting on Linux, leveraging Docker for containerization and Kubernetes for orchestration.
- Deploy and optimize AI workloads across Cloud Platforms (AWS, Azure, GCP), configuring GPU clusters for cost-effective scaling.
- Implement AI Workload Orchestration tools to schedule, manage, and monitor distributed training jobs across multi-node setups.
- Build High-Performance Computing (HPC) systems with Distributed Systems expertise, focusing on low-latency Storage & Networking for AI (e.g., NVMe, InfiniBand).
- Provision infrastructure using Infrastructure as Code (Terraform), ensuring reproducible and version-controlled deployments.
- Establish CI/CD pipelines with Git integration for automated building, testing, and rollout of AI infrastructure components.
- Set up Monitoring & Observability stacks (e.g., Prometheus, Grafana) to track GPU utilization, cluster health, and performance bottlenecks.
- Collaborate in Agile methodologies, delivering iterative improvements to AI infrastructure through sprints and cross-functional teamwork.
- Optimize resource allocation for AI pipelines, reducing costs while maximizing throughput for large-scale model training and serving.
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What This Job Offers
Job Type
Full-time
Career Level
Mid Level
Education Level
No Education Listed
