🛠 Tech Stack

💼 About This Role

You'll design and automate enterprise-grade AWS SageMaker environments and build CI/CD pipelines for ML workloads. Your work will enable data science teams to deploy models faster and more reliably in a remote-first international environment.

🎯 What You'll Do

• Design and automate AWS SageMaker environments and infrastructure.
• Build CI/CD pipelines for Docker images and ML workloads.
• Configure SageMaker lifecycle policies for ML teams.
• Create monitoring, alerting, and cost-control for SageMaker.
• Support MLOps initiatives and automate model deployments.

📋 Requirements

• 6+ years of experience in DevOps, Cloud Engineering, or MLOps.
• Expert-level knowledge of AWS services.
• Strong proficiency in Python programming.
• Hands-on experience with AWS SageMaker and ML infrastructure.

✨ Nice to Have

• Experience with Jenkins deployment pipelines.
• Familiarity with Docker and containerized environments.
• Experience with MLOps practices and model deployment automation.

🎁 Benefits & Perks

• 🌍 Remote-first work across Europe
• 🤖 Cutting-edge AI and MLOps projects
• 📄 Flexible employment options (Contractor or Full-time)
• 📈 Career growth in advanced cloud and AI technologies
• 🌐 Inclusive and diverse workplace

📨 Hiring Process

Estimated timeline: 2-4 weeks · AI estimate

1. 1Recruiter Screen· 30 min
2. 2Technical Interview· 60 min
3. 3Hiring Manager Interview· 45 min