David Adeteye

MLOps Platform Architecture: Designed and implemented a production-grade MLOps architecture integrating prompt ingestion, model inference, orchestration, and asynchronous processing, enabling reliable AI-generated greeting card workflows with low latency and high throughput. Inference & Orchestration on ECS: Deployed containerized ML inference and orchestration services on AWS ECS (internal staging), integrating FastAPI APIs, Celery workers, Redis, and inference services to handle background tasks, retries, and workload spikes securely and efficiently. Scalable & Safe Deployments: Built blue/green deployment strategies and environment separation (staging/internal) to safely iterate on models and backend logic without impacting user-facing systems. CI/CD for ML & Backend Pipelines: Designed CI/CD pipelines using GitHub Actions and GitLab CI to automate container builds, configuration management, security checks, and staged deployments for ML and backend services. Cost, Performance & Reliability Optimization: Tuned infrastructure and service configurations to minimize inference costs, reduce cold starts, and maintain low-latency responses, while ensuring horizontal scalability for seasonal traffic peaks. Security, Observability & Ops Readiness: Implemented IAM least privilege, secrets management, encryption, and end-to-end observability using CloudWatch, Datadog, Prometheus, and Grafana to monitor model performance, worker health, and pipeline reliability. Impact: Delivered a production-ready MLOps foundation that unified ML workflows with DevOps best practices, enabling secure internal experimentation, efficient inference pipelines, and a clear path to scale AI-driven customer products.

- Multi-cloud Infrastructure: Automated provisioning of AWS and Azure environments using Terraform, ensuring consistency across multi-cloud deployments for production and staging environments. Designed architecture with a focus on scalability and fault tolerance, resulting in faster setup and reduced configuration drift. - Azure & AWS Hybrid Setup: Deployed a hybrid infrastructure with Azure VMs, App Services, and AWS EC2, ensuring seamless interaction between the two platforms for high availability and redundancy. This setup helped maintain consistent operations across both cloud platforms, enabling load balancing during peak traffic times. - Kubernetes & Containerization: Managed EKS clusters on AWS and GKE in GCP, deploying containerized applications with Helm and implementing high availability with load balancing and autoscaling. - CI/CD Pipeline Automation: Designed and automated CI/CD pipelines for multi-cloud environments using GitLab CI and GitHub Actions, integrating tools like SonarQube, Snyk, and Trivy to scan for vulnerabilities before production deployment. - Monitoring & Logging: Built and managed observability stacks with Datadog, Prometheus, and Grafana across both AWS and Azure environments. Proactively set up monitoring for critical services, reducing downtime by enabling real-time alerts and quick issue resolution. Integrated CloudWatch for AWS and Azure Monitor for Azure services to create a unified monitoring system. - Security & Compliance: Integrated AWS Config, GuardDuty, and Security Hub into CI/CD pipelines for continuous security checks, while also applying Azure Security Center and Azure Defender to ensure a consistent security posture across both cloud platforms.

High Availability Architecture: Designed AWS infrastructure across multiple AZs using EC2, ALB, Route 53, and Auto Scaling, sustaining 99.98% uptime SLA for production services. - Configuration Management: Automated provisioning and patching for 20+ EC2 instances with Ansible, cutting deployment time by 75% and ensuring CIS Benchmark compliance. - Security Hardening: Deployed AWS WAF, GuardDuty, and KMS encryption for sensitive workloads, reducing potential attack vectors by 60%. - Blue-Green Deployments: Implemented GitLab CI-driven blue-green deployments, lowering release-related outages by 35%.- Monitoring & Logging: Configured CloudWatch, ELK Stack, and Grafana dashboards for live production insights, enabling incident resolution in under 30 minutes.

- AWS Optimization: Managed EC2, RDS, Lambda, VPC, and IAM configurations, cutting monthly spend by 30% using AWS Trusted Advisor and cost optimization strategies. - Serverless Deployment: Built API Gateway + Lambda + DynamoDB stack for a high-throughput application, delivering sub-100ms response times. - Security Enforcement: Applied IAM least privilege, NACLs, and MFA for all developer accounts, achieving full compliance with internal security audits. - Scalability Improvements: Tuned Auto Scaling policies for EC2 instances, maintaining 100% uptime during traffic surges without over-provisioning.