Related skills
docker aws python kubernetes tensorflow📋 Description
- Design, develop, and maintain back-end services and APIs.
- Collaborate with ML engineers to deploy models in production.
- Build scalable systems for large data volumes and performance.
- Lead ML infra improvements, automate pipelines and workflows.
- Implement testing, monitoring, and deployment best practices.
- Participate in architectural decisions for long-term scalability.
🎯 Requirements
- Backend engineer with API and scalable services.
- Python and ML frameworks (TensorFlow, PyTorch).
- Production ML model deployment experience.
- Distributed systems, data pipelines, and cloud platforms (AWS, GCP, Azure).
- Docker and Kubernetes for containers and orchestration.
- SQL and NoSQL databases with optimized storage and retrieval.
🎁 Benefits
- Competitive salary and equity.
- 5-week vacation plus 2 RTT.
- Paid sick leave.
- Paid parental leave (16 weeks).
- MacBook Pro provided.
- Swile card for meals.
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