Vizzia
Cloud Engineer - AI
2mo ago
EuropeRemoteawscdkgithub actionsiacci/cddevopsmlops
Design and run cloud architecture, optimize AI pipelines, and develop CI/CD for the AI team.
Requirements
- Engineering degree (Bac+5) or equivalent in computer science.
- At least 3 years in DevOps or Cloud Engineering, with concrete production work on AWS.
- Comfortable with IaC (Terraform or CDK), CI/CD, and Docker.
- Working knowledge of one or more of the following languages Python, Go, Rust, TypeScript, or Bash.
- Hands-on, structured, and at ease in incident and post-mortem culture.
Conditions
- 💰 Compensation: €70K–€90K+ annually
- 🏡 Hybrid work
- 🏝 Contrat cadre and RTT (between 8 and 12 days per year depending on public holidays)
- 💻 A Mac or PC depending on your preferences
- 💸 BSPCE
- 🍜 60% coverage of meal vouchers worth €9 per worked day
- 🚃/🚲 Sustainable mobility allowance
- 🏥 Mutuelle (Alan)
- 💼 Offices located in central Paris (9th arrondissement)
- ☀️ Annual offsite with the whole team and plenty of company events
- If you've read this far, you're probably very interested in the role and/or Vizzia. We'd like to hear from you even if you feel ou don't check every box.
Other
- 250+ local authorities rely on Vizzia to keep their streets clean and safe, with the ambition to reach 500+ by the end of 2026. AI Waste is the product where computer vision turns city signals into action on the ground.
- The AI team (15 people) is moving from 400 to 1000+ cameras in production, dividing AI processing cost by 5, and onboarding new models in parallel. None of that ships without a cloud infrastructure that scales, holds under load, and stays cheap to run.
- The Infra Orchestration squad sits inside the AI team and owns the cloud, the orchestration layer, and the CI/CD that the AI engineers ship through every day.
- We're hiring a Cloud Engineer reporting to Bruno, Head of AI, to design and run the cloud architecture, optimize AI pipelines, and ship the CI/CD that makes the AI team faster.
- Design and maintain cloud architectures and IaC (CDK) for the AI Waste stack on AWS.
- Make new cloud resources reproducible and easy to deploy across environments.
- Orchestrate and optimize the AI processing pipelines used by the AI team.
- Build orchestration and error-management components that hold under high data volumes.
- Develop and optimize the CI/CD pipelines used by the AI team (GitHub Actions).
- Promote DevOps and MLOps best practices, automate manual workflows.
- Keep the infrastructure scalable, secure, and observable as the camera fleet grows from 400 to 1000.
- Contribute to incident response and post-mortems in a cloud culture.
- Track and optimize cloud cost; help divide AI processing cost by 5 over the year.