Zikora Orakwe, M.S.C, MMAI

On a 4-month part-time contract, leading the design and implementation of a comprehensive LLM-powered chatbot solution for a prominent provider of retail software solutions. The project encompasses the development, deployment, and monitoring of an advanced Generative AI system tailored to the retail industry: Development: Architecting a scalable and modular LLM framework to address retail-specific use cases. Fine-tuning transformer models using domain-specific datasets to enhance contextual accuracy and relevance. Implementing RAG (Retrieval-Augmented Generation) techniques to integrate domain knowledge into the LLM workflow seamlessly. Designing reusable components and APIs for easy integration into the company’s technology stack. Deployment: Establishing a CI/CD pipeline for streamlined testing and deployment, enabling faster iterations and reliable production releases. Leveraging containerization (Docker) and orchestration (Kubernetes) for scalable and efficient cloud-based deployment. Implementing secure storage solutions to protect model artifacts and ensure data compliance with privacy regulations. Developing cost optimization strategies to manage resource allocation and ensure financial viability. Monitoring: Setting up real-time monitoring and logging systems using AWS CloudWatch and ELK Stack to track performance, latency, and availability. Building anomaly detection mechanisms to identify performance drifts and maintain system reliability. Creating dynamic dashboards for performance analytics and setting up alerting mechanisms for efficient troubleshooting. Establishing an LLMOps framework to support ongoing updates, retraining workflows, and long-term operational efficiency.

• Deployed machine learning models for predictive analytics as a Cloud Engineer (Data & AI). • Applied Kubernetes RBAC and network policies to enforce security standards. • Architected scalable cloud infrastructure with Terraform and AWS CloudFormation. • Integrated PaaS solutions to enhance business operations and data-driven applications. • Increased system scalability and resilience through DynamoDB expertise. • Optimised development workflows by implementing Jenkins pipelines for Data & AI services. • Designed data warehousing solutions with Redshift for AI applications. • Presented business cases for cloud engineering projects to non-technical audiences. • Collaborated with DevOps and business stakeholders to align cloud solutions with objectives. • Managed high-availability storage for reliable AI data access. 𝐂𝐨𝐫𝐞 𝐀𝐜𝐜𝐨𝐦𝐩𝐥𝐢𝐬𝐡𝐦𝐞𝐧𝐭𝐬: • Streamlined development using Scrum, reducing delivery times by 20%. • Improved code quality and reduced bug resolution time by 30% with automated testing. • Leveraged research techniques to solve coding issues, increasing efficiency by 25%. • Led an engineering team to build AI solutions that cut costs by over 20%. • Conducted cloud audits to eliminate underutilised resources, lowering costs by 20%. • Enhanced client service by managing cloud data and AI operations efficiently. • Scaled POCs into full implementations, including serverless and containerised architectures. • Established a robust IAM strategy with MFA and role-based access across AWS accounts.

• Applied advanced research techniques, crafting cutting-edge applications and using a natural curiosity to implement the latest industry insights for optimising company software. • Implemented advanced security measures for EKS in multi-cloud environments, ensuring efficient and secure handling of large-scale datasets. • Led Kubernetes cluster upgrades and maintenance, ensuring service continuity and minimal downtime during transitions. • Maintained and optimised complex storage systems, enhancing the efficiency of cloud computing and AI integration. Used Insights to boost performance and efficiency in cloud-based AI models. • Managed version control via Git, synchronising updates across a multidisciplinary team on cloud engineering projects. • Worked closely with stakeholders to assess POC results, refine requirements, and align with strategic objectives for full-scale deployment. Designed, built, and managed resilient, cloud-based systems. 𝐂𝐨𝐫𝐞 𝐀𝐜𝐜𝐨𝐦𝐩𝐥𝐢𝐬𝐡𝐦𝐞𝐧𝐭𝐬: • Developed automated provisioning scripts, accelerating multi-tier application deployment by 30%. • Pioneered migration strategies, significantly reducing downtime during application transitions. • Designed robust cloud security frameworks, utilising AWS tools like Security Hub, Config, and Inspector to ensure industry compliance and safeguard sensitive data.

• Led a team that employed structural analysis to resolve issues within our cloud infrastructure. • Designed, developed, and maintained secure AWS environments for application deployment, with DynamoDB as a critical infrastructure component. • Used SaaS technologies to design and deploy data analytics and AI models within a cloud environment. • Enhanced system efficiency and managed migrating legacy business applications to cloud platforms through innovative data and AI solutions. • Developed and implemented disaster recovery strategies for Kubernetes workloads, incorporating backup and restore procedures using tools like Velero. • Served as a subject matter expert in cloud architecture, guiding successful project outcomes. • Implemented VPC, security groups, and NACLs to regulate traffic and bolster the security of cloud environments, minimising the risk of unauthorised access. • Leveraged AWS cost management tools, including Trusted Advisor and Cost Explorer, to optimise resource allocation and develop budget-conscious cloud strategies. 𝐂𝐨𝐫𝐞 𝐀𝐜𝐜𝐨𝐦𝐩𝐥𝐢𝐬𝐡𝐦𝐞𝐧𝐭𝐬: • Implemented AWS best practices to lead cloud migration projects, achieving a nearly one-third reduction in operational costs. • Leveraged innovative skills to design and deploy new algorithms, enhancing system performance by 30%. • Applied CI/CD principles within a diverse development team, ensuring high code quality and achieving regular deployment schedules, which reduced deployment time by 5%. • Optimised database performance using SQL, resolving legacy code issues across various software engineering roles, leading to a 20% improved system efficiency.

• Implemented CI/CD pipelines to automate software builds, testing, and deployment, significantly enhancing efficiency and accelerating product delivery. • Executed advanced engineering practices in configuring, managing, and deploying AWS cloud environments while mentoring peers in DevOps and cloud computing technologies. 𝐂𝐨𝐫𝐞 𝐀𝐜𝐜𝐨𝐦𝐩𝐥𝐢𝐬𝐡𝐦𝐞𝐧𝐭𝐬: • Oversaw data storage and processing infrastructure in the cloud using Linux, resulting in a 20% improvement in system performance. • Deployed GPUs for complex algorithm computations that boosted software performance by 30% while ensuring data integrity across multiple databases, significantly reducing discrepancies.

• Executed solution design principles to create a suite of software applications, boosting efficiency by 40%. Utilised AWS SQS message queues to organise system requests, ensuring stability. • Improved system uptime through cloud infrastructure management, leveraging Infrastructure as Code tools like Terraform and Ansible to reduce deployment times by 5%. • Developed and enforced IAM policies to manage permissions and ensure least-privilege access, cutting security vulnerabilities by 25%.