인프라·DevOps 스킬

Set up ArgoCD to keep Kubernetes deployments aligned with configuration stored in Git, an approach called GitOps. Follow guidance for multiple environments, automatic synchronization, deployment ordering, access permissions, and troubleshooting differences.

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• Set up Kubernetes deployments driven by Git changes
• Manage staging and production configurations
• Configure synchronization order and pre-deployment jobs

Convert existing scripts, CLI tools, or Crawlee projects into reusable Apify Actors, programs packaged as Docker images. Define JSON inputs and outputs, integrate the SDK, test locally, and deploy to Apify.

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• Turning an existing script or CLI tool into an Actor
• Migrating a Crawlee project to Apify
• Defining JSON inputs and structured outputs

Publish websites and web apps to AppDeploy and obtain a public URL, including apps with backend APIs, databases, and file storage. The workflow covers templates, deployment, updates, status checks, and reviewing errors through its HTTP API.

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• Publishing a website at a public URL
• Deploying an app with a backend
• Updating an already deployed app

Design production alerts, route notifications by severity, and organize on-call schedules. Use configuration examples to define backup contacts, escalation steps when alerts go unanswered, and runbooks that guide responders through incident investigation.

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• Define alert thresholds and severity levels
• Route notifications to Slack and PagerDuty
• Plan on-call rotations and holiday coverage

Guide AAS maintainers through pull request review, batch merging, generated-file synchronization, and scripted releases. Follow protected-branch procedures and check evidence tied to the exact revision before accepting changes.

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• Reviewing and merging a batch of pull requests
• Synchronizing generated files after source changes
• Preparing and publishing a protected release

Design traffic controls for AI inference services using Istio configuration examples. Covers mutual authentication, model version routing, gradual releases, retry limits, failure containment, and monitoring, with guidance for validating changes on the target cluster.

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• Secure communication between inference services
• Gradually route requests to a new model version
• Review retries and failure containment for GPU services

Design scheduled AI workflows for document ingestion, batch processing, and model training using Prefect, Airflow, or Dagster. Compare orchestration tools and plan task dependencies, retries, caching, and monitoring through implementation fragments.

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• Schedule document ingestion and RAG index refreshes
• Design batch workflows for LLM requests
• Connect model training with quality evaluation

Create container-based hosted agents in Azure AI Foundry using Python and the Azure AI Projects SDK. Follow examples for resource settings, authentication, version management, and checks needed before treating a deployment as healthy.

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• Creating a hosted agent from a custom container image
• Configuring CPU, memory, and environment variables
• Listing and deleting agent versions

Develop incident response procedures for LLM outages, declining answer quality, spending spikes, and unsafe outputs. Use example monitoring alerts, severity levels, recovery playbooks, and postmortem guidance to organize detection, response, and follow-up work.

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• Plan fallback procedures for model provider outages
• Investigate declining answer quality after a deployment
• Prepare emergency controls for token spending spikes

Add monitoring to AI agents with response times, token usage, estimated costs, and error metrics. Trace records follow model and tool calls step by step, helping teams investigate failures and understand operating costs.

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• Investigate slow agent responses
• Track token usage and estimated costs
• Trace failures in model and tool calls

Guide reviews of Linux Kernel patches for memory safety, Terraform plans for costly resource changes, and Kubernetes configurations for restricted permissions. A separately installed tool and configured AI model produce advisory findings and candidate manifests for validation.

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• Review Linux Kernel patches for memory safety issues
• Check Terraform plans for potentially costly resource changes
• Draft Kubernetes permission settings from security requirements

Manage container images in Azure Container Registry using Python. Inspect repositories, tags, and image metadata, protect images from deletion, download image data, and identify older untagged images for cleanup.

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• Inspect image tags and metadata
• Protect production images from deletion
• Clean up older untagged images

Implement large parallel workloads in Java with Azure Batch. Manage groups of computing machines, organize jobs and tasks, inspect progress and output, adjust computing capacity, and schedule recurring jobs using SDK examples.

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• Running many computing tasks in parallel
• Adjusting machine counts to match pending work
• Checking task progress, exit codes, and output

Prepare the next Prisma 8 release by updating workspace versions, assembling upgrade guides, and drafting release notes. Open release and documentation pull requests for maintainer review; merging the release request triggers automated publication.

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• Preparing the next Prisma 8 release candidate
• Updating versions across workspace packages
• Assembling upgrade guides and release notes

Check release readiness inside the herdr repository by comparing changes since the base release with upcoming release notes and documentation. Review CLI descriptions, issue references, checks, and benchmarks, then report readiness.

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• Check release notes against changes since the base release
• Review upcoming documentation before a release
• Inspect issue references that release CI will close

Manage Sub2API through its administrator API using a bundled command-line tool. Inspect and update accounts, manage redeem codes, groups, and proxies, import or export accounts, and verify changes with follow-up reads.

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• Reviewing account details and usage before updating settings
• Importing, exporting, or updating multiple accounts
• Generating and redeeming recharge or balance codes

Access and test protected Vercel deployments with agent-browser using an existing Vercel identity and a short-lived authentication token. Keep deployment protection enabled while diagnosing login redirects, environment access mismatches, and application authentication errors.

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• Testing a protected Preview deployment
• Diagnosing Vercel login redirects and protection errors
• Identifying access rules needed for Production testing

Add browser automation to Vercel apps by running agent-browser and headless Chrome in temporary, isolated virtual machines. Capture screenshots, inspect page structure, and complete multi-step forms while preserving browser state between commands.

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• Add website screenshot capture to a Vercel app
• Fill and submit forms across multiple browser commands
• Run recurring browser tasks with Vercel Cron Jobs

Automate page interactions and screenshots with agent-browser running in AWS Bedrock AgentCore cloud browsers. Use standard browser commands, preserve login state across sessions with persistent profiles, and watch active sessions through an AWS Console link.

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• Run browser automation on AWS
• Reuse login state across browser sessions
• Watch a cloud browser session live

Configure Prometheus to collect and store operational metrics from infrastructure and applications. Covers collection schedules, alert rules, automatic discovery of monitored services, and troubleshooting checks to help establish and maintain monitoring.

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• Setting up infrastructure and application monitoring
• Configuring metric collection intervals and retention
• Designing alert rules

Build Grafana dashboards that show application and infrastructure health through request rates, errors, response times, and resource usage. Organize metrics into useful panels, add filters and alerts, and manage dashboard definitions as code.

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• Build an API health dashboard
• Visualize CPU and memory usage
• Track service level objectives and business KPIs

Define service reliability measurements and targets, then turn them into Prometheus monitoring rules and alerts. Calculate error budgets, the amount of failure allowed, and outline Grafana dashboards for tracking reliability and deciding when to prioritize fixes.

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• Setting availability and response time targets
• Calculating and tracking error budgets
• Creating alerts for rapid error budget consumption

Set up distributed tracing with Jaeger and Tempo to follow requests across services. Examine delays, service dependencies, and error propagation, with guidance on sampling, linking logs to traces, and troubleshooting missing trace data.

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• Investigating slow requests across services
• Understanding service dependencies
• Tracing how errors spread

Add structured logs, performance metrics, and traces that follow requests across Python services. Establish consistent log fields and request IDs so production failures can be investigated using recorded activity and service health signals.

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• Adding searchable JSON logs
• Collecting service metrics with Prometheus
• Following requests across multiple services