Emmanuel Ahaiwe, a Nigerian researcher and DevOps engineer, is gaining attention for his work at the intersection of artificial intelligence and enterprise operations, particularly within financial technologies and cloud-based systems. With a Master’s degree in Artificial Intelligence and Machine Learning from the University of Portsmouth, Ahaiwe has increasingly focused on combining MLOps and DevOps to improve automation, security, and infrastructure resilience.
Currently serving as Lead DevOps Engineer for a UK-based broadband provider, Ahaiwe has overseen the reconstruction of a critical DevOps team following a company-wide restructuring. He designed an AI-driven automation platform for the firm’s Geographic Information System (QGIS), which maps broadband delivery coverage across the United Kingdom. This integration has reduced Salesforce deployment timelines from several weeks to a single day, illustrating the practical impact of AI-enhanced workflows.
At Korapay, where he is Senior DevOps Engineer, Ahaiwe re-architected the company’s DevOps infrastructure to accommodate a reported 334 per cent increase in transactions in 2024. His multi-cloud failover strategies, disaster recovery protocols, and automation frameworks have reportedly strengthened the resilience of fintech systems, allowing for secure scalability without vendor dependency.
His prior roles at Sparkle Microfinance Bank and Qore Banking also reflect a consistent focus on modernizing legacy systems. At Sparkle, he developed containerized applications and GitOps pipelines on Azure Kubernetes Service, while at Qore, he led the adoption of Kubernetes-powered infrastructure to replace virtual machine-based systems. According to Ahaiwe, this migration “safeguarded uptime and reduced operational bottlenecks, laying the groundwork for cloud-native financial operations.”
Ahaiwe’s work extends beyond the corporate environment. He has presented at major conferences such as DevOps Live London and DevOpsDays London 2024, where he discussed the integration of AI into DevOps practices and the role of MLOps in enterprise environments. He also participated in CIVO Navigate Germany, a conference on cloud-native innovation.
In addition to his professional accomplishments, Ahaiwe has produced academic research exploring the application of AI to operational systems in financial services and broadband infrastructure. He stresses that artificial intelligence is most effective when integrated with DevOps practices to automate workflows and enhance predictive capabilities. “Financial institutions cannot fully realise AI’s promise without robust MLOps frameworks embedded in their DevOps strategies,” he said.
Ahaiwe is preparing to publish How to Do DevOps Properly for a Fintech, described as both an MLOps handbook and a technical hiring guide. The book is intended to provide practical guidance for startups and established institutions on structuring resilient, scalable, and AI-enabled infrastructure.
Beyond technical implementation, Ahaiwe places emphasis on team sustainability. He has instituted knowledge transfer protocols and documentation standards to reduce “key man risk,” ensuring continuity in operations and reducing dependency on individual expertise. His technical competencies include cloud platforms such as AWS, Azure, and Google Cloud, container orchestration systems including Kubernetes and Docker, CI/CD tools like Jenkins and ArgoCD, and monitoring solutions such as Grafana and Datadog.
In a sector where downtime and cyber threats carry significant financial risk, Ahaiwe’s integration of AI, multi-cloud security, and disaster recovery strategies has provided tangible benefits. He also advocates for a cultural shift within DevOps, emphasising collaboration across traditionally siloed teams to enhance operational efficiency.
Through his work, Ahaiwe exemplifies a new generation of DevOps professionals capable of merging applied artificial intelligence with infrastructure management, demonstrating both technical depth and strategic foresight in the rapidly evolving fintech and technology landscape.
