Responsibilities
- Work directly with the ML Engineers team to put Machine Learning models into production using and creating ML pipelines
- Implement DevOps SRE metrics
- Administration and monitoring of team platforms
- Responsible for the infrastructure and its proper functioning
- Operation and monitoring of our applications
- To be the main promoter of practices and operationalization of the necessary pieces to serve our models and put them to interact with the rest of the company in a real environment and highly scalable
- Promote a technical culture driving data products with DevOps, SRE and MLOps
Requirements
- At least 3 year of experience in work environments as Software Engineer, DevOps Engineer, SRE or ML Engineer
- Experience with Python
- Experience working in UNIX / LINUX environments
- Experience creating CI / CD pipelines
- Experience working with Docker
- Deploying, managing, and scaling AI models and applications in production environments using DevOps principles
- Experience in GIT
- Good spoken English
- Experience working with cloud services, preferably GCP
- Excellent communication skills and collaborative work
Nice to have
- Experience in Kubernetes
- Experience designing and implementing automated tests
- Experience in observability, monitoring
- Understanding of the complete life flow of Machine Learning models
- Experience in infrastructure as code, desirable Terraform
- Experience with Jenkins
- Agility to visualize possible improvements, problems, and solutions in Architecture
- Experience with monitoring tools such as Grafana, Datadog, Nagios
Useful links
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