Position Objective
Ensure the industrialization automation and reliability of data science pipelines in a hybrid AWS/GCP cloud environment.
Main Responsibilities
DevOps
Design and maintain CI/CD pipelines (GitLab CI GitHub Actions Jenkins).
Manage infrastructure using Terraform.
Deploy and monitor containerized applications (Docker Kubernetes Helm).
Implement monitoring solutions (Prometheus Grafana CloudWatch Stackdriver).
Secure cloud environments (IAM KMS secret management).
MLOps
Develop and orchestrate ML & AI pipelines (Airflow Vertex AI Pipelines etc.).
Deploy models to production (Vertex AI Endpoints).
Collaborate with data scientists to automate the model lifecycle.
Technical Environment
Cloud: AWS (S3 SageMaker EKS) GCP (BigQuery Vertex AI GKE)
IaC: Terraform
SaaS: Snowflake
PaaS: Dataiku
CI/CD: GitLab CI GitHub Actions
Containers: Docker Kubernetes
Languages: Python Bash YAML
Monitoring: Prometheus Grafana CloudWatch Stackdriver
ML Tools: Airflow Kubeflow Vertex AI SageMaker
Qualifications :
Proven experience in DevOps and/or MLOps.
Strong expertise in AWS and GCP cloud environments.
Knowledge of cloud security best practices.
Ability to collaborate effectively with developers data engineers and data scientists.
Strong automation mindset rigor and focus on observability.
Proficient in spoken English.
Additional Information :
Why Join Us
A collaborative and supportive team.
High-impact projects for a client who is a leader in their field.
A stimulating environment where your expertise will be recognized and valued.
Are you ready to take on this challenge Send us your application
Remote Work :
No
Employment Type :
Full-time
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