Devops (Gcp) Engineer, Airline Services

Project overview: The Advanced Analytics management is a transformational team within our client whose objective is to generate measurable value that lasts over time across all areas of the company. To achieve this, custom data products are created based on analytical models, mostly from ML but being able to use a broader spectrum of techniques; either by delivering new analytical capabilities to the areas you work with or by directly interacting with your systems.
- The development of the products and the associated change management is carried out using Agile methodologies (development) that incorporate the scientific method and LEAN tools (change management). To ensure the correct execution of our products, we work under a DevOps environment using the MLOps and DevOps SRE practices, guaranteeing the quality, security, and compliance of the company.
- 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
- 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 1 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
- Experience in GIT
- Intermediate/advanced oral and written 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