aws-ai-services-expert
Build AI applications on AWS using Bedrock, SageMaker, and AI/ML services with best practices for enterprise deployment
Build AI applications on AWS using Bedrock, SageMaker, and AI/ML services with best practices for enterprise deployment
Production-grade ML infrastructure with Kubernetes, auto-scaling, and cost optimization
Engineer resilient UI systems with layered error boundaries, retries, and fallback orchestration.
Expert-level DevOps practices, culture, automation, and continuous delivery
Advanced service mesh implementation with Istio, Linkerd, traffic management, mTLS, and observability.
Build cloud-native microservices - service discovery, config server, API gateway, resilience patterns
Microsoft Azure architecture patterns and best practices. Use when designing, deploying, or reviewing Azure infrastructure including AKS, App Service, Functions, CosmosDB, and Entra ID.
CI/CD pipelines, infrastructure as code, and deployment strategies
Automated deployment orchestration with rollback, blue-green, and canary deployment strategies
Infrastructure, deployment, and operations patterns for Docker, Kubernetes, and CI/CD
Apply when containerizing applications: writing Dockerfiles, docker-compose configurations, and multi-stage builds.
GitOps deployment patterns with ArgoCD and Flux. Use when implementing Git-based infrastructure management, continuous deployment, or declarative operations.
Enterprise monorepo patterns with Turborepo and Nx including task orchestration, caching, and CI/CD optimization
Alibaba Cloud architecture patterns and best practices. Use when designing, deploying, or reviewing infrastructure on Alibaba Cloud including ECS, ACK, Function Compute, and OSS.
Service discovery patterns with Consul, Kubernetes DNS, Eureka, health checks, and client-side load balancing.
Develop custom Terraform and OpenTofu providers using the Plugin Framework. Use when creating new providers, implementing CRUD operations, writing acceptance tests, debugging provider issues, or migrating from SDKv2 to Plugin Framework. Covers TDD workflow, resource/data source patterns, and terraform-plugin-testing.
Scaffold and automate Grafana plugin projects using @grafana/create-plugin. Use when creating panel plugins, data source plugins, app plugins, or backend plugins. Handles project scaffolding, Docker dev environment setup, and plugin configuration.
Vercel deployment and configuration for Adynato projects. Covers environment variables, vercel.json, project linking, common errors like VERCEL_ORG_ID/VERCEL_PROJECT_ID, and CI/CD setup. Use when deploying to Vercel, configuring builds, or troubleshooting deployment issues.
Staging and production deployment using Laravel Deployer with health checks, cache management, and rollback support (HUMAN-ONLY execution)
Vercel deployment patterns and best practices. Use when deploying frontend applications, configuring edge functions, setting up preview deployments, or optimizing Next.js applications.
Monitor and interact with Vercel deployments, build logs, and environment details using the Vercel CLI. Use this to check build status, wait for deployments, view logs, and debug CI/CD failures.
Create Dockerfiles, docker-compose configurations, and container workflows for Python projects with UV. Use when containerizing applications, setting up development environments, or deploying with Docker.
Next.js deployment - Vercel, Docker, self-hosting strategies