job description
Are you passionate about transforming cutting-edge AI concepts into scalable enterprise solutions? Join our dynamic team as an AI Delivery Engineer - Cloud and take the lead in implementing and deploying intelligent systems that drive business innovation. Based in the vibrant tech hubs of Bali, you'll collaborate with cross-functional teams to deliver high-impact AI solutions tailored to enterprise needs.
In this role, you'll bridge the gap between advanced AI research and real-world applications, ensuring seamless integration with cloud platforms. Your expertise will shape the future of intelligent automation, helping businesses unlock new efficiencies and competitive advantages. If you thrive in a fast-paced environment and enjoy solving complex technical challenges, this is your opportunity to make a lasting impact.
We offer a competitive salary, a collaborative work culture, and the chance to work on groundbreaking projects in one of the world's most inspiring locations. Apply now and be part of the AI revolution!
Responsibility
- Lead the end-to-end implementation and deployment of AI-driven enterprise solutions on cloud platforms.
- Collaborate with data scientists, developers, and business stakeholders to translate AI models into production-ready systems.
- Design and optimize cloud infrastructure to support scalable AI workloads, ensuring high performance and reliability.
- Develop and maintain CI/CD pipelines for AI model deployment, monitoring, and versioning.
- Troubleshoot and resolve technical issues related to AI integration, performance bottlenecks, and system failures.
- Ensure compliance with security and data governance policies in AI deployments.
- Provide technical guidance and mentorship to junior engineers and cross-functional teams.
- Stay updated with emerging AI and cloud technologies to drive continuous improvement in delivery processes.
Qualifications
- Bachelor's or Master's degree in Computer Science, Engineering, or a related field.
- Proven experience in deploying AI/ML models in cloud environments (AWS, Azure, or GCP).
- Strong proficiency in programming languages such as Python, Java, or Scala.
- Experience with containerization (Docker, Kubernetes) and infrastructure-as-code tools (Terraform, Ansible).
- Familiarity with MLOps practices and tools for model lifecycle management.
- Excellent problem-solving skills and the ability to work in a fast-paced, collaborative environment.
- Strong communication skills to articulate technical concepts to non-technical stakeholders.
- Certifications in cloud platforms (e.g., AWS Certified, Azure Solutions Architect) are a plus.