Tasks:
- * Automate development and operations processes
- * Build end-to-end machine learning pipelines for Edge AI applications
- * Define DevOps and MLOps metrics
- * Define secure coding practices
- * Define workflows for Edge AI application development
- * Deploy machine learning models to production using CI/CD and containers
- * Design and build ML DevOps infrastructure
- * Identify tooling and automation needs with cross-functional teams
- * Implement repository management strategies
- * Monitor development efficiency and operational performance
Perks/Benefits:
- + Hybrid working
- + Recruitment accommodations
Skills/Tech stack required:
[Ansible] [AWS] [Azure] [C#] [C++] [CI/CD] [Continuous Training] [Continuous Training (CT)] [Docker] [Elasticsearch] [Embedded Systems] [Embedded systems testing] [Gerrit] [Git] [GitHub Actions] [GitLab CI] [Grafana] [Hardware-in-the-loop] [Hardware-in-the-loop (HIL)] [Infrastructure as Code] [Jenkins] [Kibana] [Kubernetes] [Linux] [Machine Learning] [Machine Learning Pipelines] [MLOps] [Packer] [Prometheus] [Python] [PyTorch] [Repository Management] [Software-in-the-loop] [Software-in-the-loop (SIL)] [Systems testing] [Terraform] [Version control]
Educational requirements:
N/A
Role(s):
[AI Engineer] [DevOps Engineer] [Engineer] [Machine Learning DevOps Engineer] [MLOps Engineer] [Senior AI Engineer] [Senior Machine Learning DevOps Engineer]