George O.

As an AWS-certified DevOps and cloud security engineer, I help startups, fintech companies, and AI/ML platforms build secure, compliant, production-grade AWS infrastructure — including the AI infrastructure layer most DevOps engineers haven't touched yet: LLM inference endpoints, RAG pipelines, and GPU-backed compute. With certifications in AWS DevOps Engineer – Professional, AWS Solutions Architect – Professional, AWS Security – Specialty and AWS CloudOps Engineer – Associate, your infrastructure is in capable, currently-certified hands. WHAT I DO BEST AI/ML Infrastructure on AWS I deploy fine-tuned LLM inference endpoints across GPU instance families, build RAG knowledge bases using Amazon Bedrock and pgvector, and stand up Feature Stores for ML pipelines — all on Terraform, all production-grade. If your platform runs AI workloads, I build the infrastructure underneath the model. Enterprise Cloud Foundation & Governance I establish AWS cloud foundations using Control Tower and Landing Zone with automated multi-account environments and security guardrails — Security Hub, GuardDuty, AWS Config, CloudTrail, and SCPs. This significantly eases SOC 2, ISO 27001, and HIPAA audit preparation through automated compliance evidence. Security-First Infrastructure VPC isolation, least-privilege IAM, secrets management, and encryption everywhere. A recent project passed a SOC 2 audit with zero infrastructure findings, built in from day one. Cloud Migrations & Containerization I migrate applications from on-premises and legacy clouds (GCP, Azure) to AWS with zero downtime — deploying to ECS/Fargate, EKS, or traditional EC2, with a focus on cost efficiency and security. Infrastructure as Code Everything is Terraform-based, version-controlled, and reproducible — multi-account Control Tower setups, VPC architectures, IAM policies, all defined as code with peer review. CI/CD & Production Reliability Deployment pipelines (GitHub Actions, GitLab CI, AWS CodePipeline) that cut deployment time from hours to minutes, with automated testing and rollback. Designed for 99.99% uptime through Multi-AZ deployments and auto-scaling. RECENT RESULTS - Architected GCP-to-AWS migration for an AI/ML fintech platform — 5 fine-tuned LLM inference endpoints across GPU instance families, RAG knowledge base on pgvector, full Terraform IaC - Deployed a containerized Spring Boot app on ECS Fargate with Control Tower multi-account setup, Security Hub, GuardDuty, Config, and CloudTrail across dev/staging/prod - Migrated a payment platform to AWS — 99.99% uptime, handled 10x traffic spikes, 40% cost reduction - Delivered enterprise Landing Zone migration framework for 85 applications, 450 VMs, 55 databases - Automated deployments from 2 hours to 15 minutes with improved security posture TECH STACK AWS AI/ML: Amazon Bedrock, Amazon SageMaker, RAG pipelines, pgvector, Feature Store, GPU instances AWS Governance & Security: Control Tower, Organizations, Security Hub, GuardDuty, Config, CloudTrail, IAM, Secrets Manager, KMS, WAF AWS Infrastructure: ECS/Fargate, EC2, Lambda, RDS, DynamoDB, S3, VPC, CloudFront, ALB/NLB, Auto Scaling Containers & Orchestration: Docker, ECS/Fargate, Kubernetes/EKS, ECR Infrastructure as Code: Terraform (5+ years), CloudFormation, AWS CDK CI/CD: GitHub Actions, GitLab CI, Jenkins, AWS CodePipeline/CodeBuild/CodeDeploy Databases: RDS (PostgreSQL, MySQL, SQL Server), DynamoDB, MongoDB/DocumentDB Monitoring: CloudWatch, CloudTrail, VPC Flow Logs, Security Hub, Config Scripting: Python, Bash, Shell WHO I WORK WITH - AI/ML platforms needing production-grade inference and RAG infrastructure - Startups preparing for SOC 2 certification - Fintech companies needing secure infrastructure for financial data - Healthcare/SaaS companies requiring HIPAA-ready environments - Companies migrating from on-premises, GCP, or Azure to AWS - Enterprises needing multi-account AWS governance WHAT MAKES ME DIFFERENT Most DevOps engineers deploy applications. I build enterprise security foundations — and the AI infrastructure layer most of them haven't touched yet. You get: ✓ AI/ML infrastructure built by someone who understands both the model and the cloud underneath it ✓ Security and compliance built in from day one ✓ Automated audit evidence collection ✓ Multi-account governance preventing drift ✓ Clear documentation and knowledge transfer Looking for secure, audit-ready AWS infrastructure — or AI/ML platform infrastructure that actually scales? Let's talk.