- Company Name
- Conserto
- Job Title
- Ingénieur DevOps/Ingénieure DevOps
- Job Description
-
**Job Title**
DevOps Engineer – MLOps
**Role Summary**
Collaborate with Data Science and DevOps teams to industrialise machine‑learning models, automate end‑to‑end data pipelines, deploy models to production, and continuously monitor and optimise performance across on‑premises and cloud infrastructures.
**Expectations**
- Minimum 3 years of DevOps/MLOps experience, preferably in banking, insurance, or financial services.
- Proactive, autonomous, and solution‑oriented.
- Strong command of MLOps toolchain and cloud platforms, with a growth mindset for new technologies.
**Key Responsibilities**
- Design, build, and maintain CI/CD pipelines for ML workflows (model training, validation, and deployment).
- Develop and orchestrate data pipelines using Kubernetes, Docker, and OpenShift.
- Deploy and manage ML/AI models with MLflow, Kubeflow, or similar frameworks.
- Implement monitoring, alerting, and logging solutions to track model performance and infra health.
- Optimize cloud and on‑premise resources for cost, scalability, and reliability.
- Lead continuous improvement of MLOps processes, tooling, and best practices.
- Collaborate closely with Data Science, Infra, and Business teams to translate model requirements into operational solutions.
**Required Skills**
- **Programming**: Python (core language), Bash/Shell.
- **CI/CD**: Jenkins, GitLab CI, or equivalent.
- **Containerisation & Orchestration**: Docker, Kubernetes, OpenShift.
- **Cloud**: AWS, GCP, or Azure – infrastructure configuration and deployment.
- **Model Management**: MLflow, Kubeflow, or comparable platforms.
- **Monitoring**: Prometheus, Grafana, ELK stack, or similar.
- **Version Control**: Git.
- **Configuration Management**: Ansible, Terraform, or equivalent.
- **Agile Methodologies**: Scrum, Kanban.
**Required Education & Certifications**
- Bachelor’s or Master’s degree in Computer Science, Software Engineering, Data Science, or a related discipline.
- Professional certifications preferred:
- AWS Certified Solutions Architect / Developer / DevOps Engineer
- Google Cloud Professional Data Engineer / Machine Learning Engineer
- Certified Kubernetes Administrator (CKA) or Certified Kubernetes Application Developer (CKAD)
- MLflow Certification or equivalent MLflow‑related training
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