Note: The job is a remote job and is open to candidates in USA. Sphere partners with global logistics companies leveraging AI, Machine Learning, and Data Engineering to optimize warehouse operations. They are seeking a Lead AI Infrastructure Engineer to build and maintain scalable AI infrastructure, enabling teams to run ML experiments and deploy machine learning models.
Responsibilities
- Design distributed training pipelines for large-scale machine learning and deep learning models
- Optimize compute and storage resources for cloud-based AI/ML workloads on AWS, GCP, or Azure
- Collaborate with data scientists and ML engineers to deploy models in production efficiently
- Implement monitoring, logging, and alerting for model performance and AI workflows
- Ensure scalable, maintainable, and reliable AI infrastructure to support real-time and batch ML applications
Skills
- 5+ years in Python and ML infrastructure
- Experience in cloud AI platforms (AWS Sagemaker, GCP AI Platform, Azure ML)
- Experience with containerization (Docker), orchestration (Kubernetes), and CI/CD for ML
- Experience with distributed systems, data pipelines, and high-performance computing for AI
- Hands-on with deep learning frameworks like TensorFlow or PyTorch
Company Overview
Company H1B Sponsorship