Platform engineer with 7 years building and operating high-availability production systems. Strong foundation in Kubernetes, cloud infrastructure (AWS/GCP), and Infrastructure as Code. Currently expanding into MLOps and data platform engineering—bringing reliability engineering principles to ML systems.

Background:

• Built and operated 99.99% uptime platforms serving 100K+ users
• Specialized in compliance-ready infrastructure (PCI-DSS, SOC2, ISO 27001)
• Led zero-downtime cloud migrations and disaster recovery implementations
• Experience with financial systems and security-critical infrastructure

Current Focus:

• MLOps: Model deployment pipelines, feature engineering infrastructure
• Data Platform Engineering: Pipeline orchestration, observability for data systems
• Platform Engineering: Kubernetes operators, GitOps, policy-as-code

Platform & Infrastructure Engineering:

• 🏗️ Production Kubernetes clusters (CKA certified) with 99.99%+ uptime
• ☁️ Cloud infrastructure (AWS/GCP) using Terraform/Pulumi
• 🔄 GitOps and CI/CD pipelines for automated deployments
• 📊 Observability: Prometheus, Grafana, Loki, distributed tracing
• 🛡️ Security: Zero-trust architecture, policy-as-code (OPA), compliance automation

MLOps & Data Platform (Expanding):

• 🤖 Model deployment infrastructure and serving layers
• 📈 ML pipeline orchestration (Airflow, Kubeflow)
• 🔧 Feature store architecture and data versioning
• 📊 ML observability and model monitoring systems

Core Skills:

• Languages: Python, Go, Bash, Rust
• Orchestration: Kubernetes, Docker, Helm
• IaC: Terraform, Pulumi, CloudFormation
• CI/CD: GitLab CI, GitHub Actions, ArgoCD
• Cloud: AWS (primary), GCP
• Observability: Prometheus, Grafana, Datadog, ELK stack
• MLOps (Learning): Kubeflow, MLflow, Airflow, SageMaker

• ✅ Maintained 99.99% uptime for production platforms serving 100K+ daily users
• ✅ Led zero-downtime cloud migration for critical financial infrastructure
• ✅ Implemented automated compliance frameworks achieving PCI-DSS and ISO 27001 certification
• ✅ Built observability pipelines reducing MTTR by 30%
• ✅ Designed disaster recovery systems with <15min RTO
• ✅ CKA Certified (Kubernetes Administrator)
• ✅ AWS Certified Solutions Architect & Data Engineer Associate

Building end-to-end ML deployment infrastructure:

• Model serving with Kubernetes + Seldon/KServe
• Feature store implementation
• ML pipeline orchestration with Airflow
• Model monitoring and drift detection
• [Repository Coming Soon]

Infrastructure for data pipelines at scale:

• Stream processing with Kafka
• Batch orchestration with Airflow
• Data quality monitoring
• [Repository Coming Soon]

Production-ready IaC and GitOps templates:

• Terraform modules for AWS/GCP
• Kubernetes operators for common patterns
• Observability stack configurations
• [Public Repository Coming Soon]

Currently studying:

• MLOps: Model deployment, feature engineering, ML observability
• Data Engineering: Stream processing, data pipeline orchestration
• Advanced Kubernetes: Operators, service mesh, multi-cluster management

Writing about:

• Platform engineering best practices
• SRE principles for ML systems
• Infrastructure automation
• 📝 Blog: Wartime Engineer

Actively seeking:

• Senior Platform Engineer roles
• MLOps Engineer positions
• Data Platform Engineer opportunities
• Staff SRE roles focused on ML/data infrastructure

Open to discussing platform engineering, MLOps, reliability engineering, or potential opportunities.

• 📧 Email: nonsoamadi@aol.com
• 💼 LinkedIn: linkedin.com/in/nonso-amadi
• 🐦 Twitter: @jackhoudini__
• 📝 Blog: Wartime Engineer