Architect- Cloud Delivery AI Platforms Full-time

Overview

We are seeking an experienced Cloud Delivery Lead to work horizontally across our organization, collaborating with Cloud Engineering, Cloud Operations, and cross-platform teams. This role is crucial in ensuring that cloud resources are delivered according to established standards, with a focus on both Azure and AWS platforms. The Cloud Delivery Lead will be responsible for architecture approvals, requirements gathering, and delivery of Data and AI platforms. This position also involves mentoring team members and driving best practices in cloud delivery across multiple platforms.

Responsibilities

1. Collaborate closely with Cloud Engineering, Cloud Operations, and cross-platform teams to ensure seamless delivery of cloud resources on both Azure and AWS.
2. Lead the architecture approval process, working with relevant stakeholders to ensure designs meet organizational standards and best practices across both Azure and AWS platforms.
3. Gather and analyze requirements from various teams, translating them into actionable cloud delivery plans.
4. Oversee the delivery of cloud resources, ensuring they adhere to approved architectures and organizational standards on both Azure and AWS.
5. Develop and maintain delivery processes and methodologies for multi-cloud deployments, with a focus on Data and AI platforms.
6. Coordinate with project managers and business stakeholders to align cloud deliveries with project timelines and business objectives.
7. Implement and maintain quality assurance processes for cloud deliveries across platforms.
8. Stay up-to-date with the latest Azure and AWS services, features, and best practices, particularly in Data and AI, and incorporate them into delivery strategies.
9. Identify and mitigate risks associated with cloud deployments and resource management in multi-cloud environments.
10. Mentor and develop team members, fostering their skills in Azure and AWS technologies, cloud delivery practices, and Data and AI platforms.
11. Create and maintain documentation for cloud delivery processes, standards, and best practices across platforms.
12. Collaborate with security teams to ensure cloud resources are deployed with appropriate security controls and comply with organizational policies in both Azure and AWS.
13. Participate in capacity planning and cost optimization initiatives for multi-cloud resources.
14. Facilitate knowledge sharing sessions and training programs to enhance the team’s expertise in Azure, AWS, and Data and AI technologies.
15. Establish and track key performance indicators (KPIs) for cloud delivery efficiency and quality across platforms.
16. Serve as a liaison between technical teams and business stakeholders, translating complex technical concepts into business value.
17. Drive the adoption of best practices for Data and AI platform delivery on both Azure and AWS.

Qualifications

• Bachelor’s degree in Computer Science, Information Technology, or a related field; Master’s degree preferred.
• 8+ years of experience in IT, with at least 6 years focused on cloud technologies, including substantial experience with both Azure and AWS.
• Strong understanding of Azure and AWS services, architectures, and best practices, particularly in Data and AI platforms.
• Certifications in both Azure (e.g., Azure Solutions Architect Expert) and AWS (e.g., AWS Certified Solutions Architect – Professional).
• Proven experience in delivering Data and AI platforms on both Azure and AWS.
• Experience in leading and mentoring technical teams across multiple cloud platforms.
• Demonstrated ability to work horizontally across different teams and platforms.
• Strong knowledge of cloud security principles and compliance requirements in multi-cloud environments.
• Excellent understanding of DevOps practices and tools applicable to both Azure and AWS.
• Experience with infrastructure as code (e.g., ARM templates, CloudFormation, Terraform).
• Proficiency in scripting languages (e.g., PowerShell, Bash, Python).
• Solid understanding of networking concepts and their implementation in Azure and AWS.