Senior Manager - Artificial Intelligence Infrastructure Architecture at MTN

Responsibilities

Key Activities & Responsibilities

• Develop a scalable AI infrastructure strategy aligned with enterprise-wide digital transformation goals, ensuring a high-performance and secure foundation for AI workloads
• Architect AI infrastructure solutions across cloud and on-prem environments, optimizing flexibility, performance, and security to meet evolving business needs
• Implement and oversee infrastructure for AI model training, inferencing, and execution frameworks that enhance processing efficiency and overall model performance
• Design and implement AI infrastructure solutions that scale seamlessly to accommodate enterprise-wide AI adoption, support business growth, ensure high availability, and incorporate future advancements in AI and cloud computing
• Establish AI infrastructure security SOPs, access control mechanisms, and compliance frameworks to mitigate risks and ensure data protection
• Design and implement automated MLOps pipelines that streamline AI model deployment, monitoring, retraining, and governance for efficient AI operations
• Deploy Infrastructure as Code (IaC) solutions using Terraform, Ansible, or equivalent tools to automate AI infrastructure provisioning and scaling
• Optimize high-performance computing environments, ensuring efficient utilization of GPU, CPU, and storage resources for AI and ML workloads
• Manage AI workload orchestration using Kubernetes and containerization technologies to enhance scalability and performance across distributed AI environments
• Enhance AI data storage and processing capabilities by optimizing pipelines, retrieval mechanisms, and data engineering strategies for real-time analytics
• Work closely with IT and Software COE teams to integrate AI infrastructure seamlessly with enterprise systems and applications
• Develop AI infrastructure cost optimization strategies, balancing performance, scalability, and budget constraints to maximize ROI
• Deploy real-time AI infrastructure monitoring tools to continuously track system health, identify performance bottlenecks, detect potential anomalies, and implement proactive optimization measures to enhance system reliability and efficiency
• Implement AI governance and model version control policies, ensuring regulatory compliance, model integrity, security, ethical AI practices, and proactive risk mitigation
• Stay ahead of AI infrastructure innovations, emerging cloud technologies, and industry best practices to enhance enterprise AI capabilities

Qualifications

Education:

• Bachelor’s degree in Computer Science, IT Infrastructure Engineering, or a related field
• Certifications in cloud computing (AWS, Azure, GCP), MLOps, or DevOps (preferred) 

Experience:

• 8+ years of experience in IT Infrastructure, Cloud Computing, or AI Systems Architecture
• Hands on experience in AI infrastructure design, cloud-based AI solutions, or MLOps
• Expertise in managing AI workloads across cloud and hybrid environments.
• Proven track record in scaling AI infrastructure for large enterprises
• Strong experience in Kubernetes, containerization, and orchestration tools
• Experience in optimizing AI workloads for performance and cost efficiency

​​​​​​​Skills:

• Expertise in AI Infrastructure & Cloud Architecture
• Strong Understanding of AI Model Deployment & MLOps
• Advanced Proficiency in Kubernetes & AI Workload Orchestration
• Hands-on Experience with Cloud Platforms (AWS, Azure, GCP)
• Proficiency in Infrastructure as Code (Terraform, Ansible)
• AI Security & Compliance Knowledge
• AI Infrastructure Cost Optimization Strategies
• Performance Tuning for AI Systems & Workloads