job description
Join SNS Network as a Senior AI Infrastructure & Systems Engineer and play a pivotal role in shaping the future of AI-driven solutions. We are seeking a highly skilled and versatile specialist to lead the deployment, orchestration, and management of our cutting-edge AI infrastructure. In this dynamic role, you will design scalable systems, optimize performance, and ensure seamless integration of AI models across our platforms.
Based in the vibrant and inspiring locations of Bali (Canggu, Ubud, Denpasar, and more), you’ll collaborate with a global team of innovators while enjoying the island’s unique work-life balance. This is a rare opportunity to combine technical excellence with a lifestyle that fuels creativity and productivity.
If you are passionate about AI infrastructure, cloud technologies, and building systems that power next-generation applications, we want to hear from you. Apply today to be part of a forward-thinking company at the forefront of AI innovation.
Responsibility
- Design, deploy, and manage scalable AI/ML infrastructure using cloud platforms (AWS, GCP, or Azure).
- Develop and optimize containerization and orchestration solutions (Docker, Kubernetes) for AI workloads.
- Implement CI/CD pipelines to automate AI model training, testing, and deployment.
- Monitor and enhance system performance, ensuring high availability and low latency for AI applications.
- Collaborate with data scientists and engineers to integrate AI models into production environments.
- Ensure security, compliance, and data privacy best practices across AI systems.
- Troubleshoot and resolve infrastructure-related issues to minimize downtime.
- Stay ahead of industry trends and evaluate new tools/technologies to improve AI infrastructure.
Qualifications
- Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
- 5+ years of experience in AI/ML infrastructure, cloud computing, or DevOps.
- Proficiency in Python, Bash, or Go for scripting and automation.
- Hands-on experience with Kubernetes, Docker, and cloud platforms (AWS, GCP, Azure).
- Strong knowledge of data pipelines, distributed systems, and microservices architecture.
- Experience with ML frameworks (TensorFlow, PyTorch) and MLOps tools (MLflow, Kubeflow).
- Familiarity with monitoring tools (Prometheus, Grafana) and logging systems (ELK Stack).
- Excellent problem-solving skills and ability to work in a fast-paced, collaborative environment.