DevOps Architect - IT Infrastructure Management

• Quality attributes of systems and their quantitative measurement
• Architectural styles of distributed systems
• Technical debt as an engineering metric
• C4 Model at the deployment level
• Architecture Decision Records (ADR) in a team environment
• Architecture Decision Records (ADR) in a team environment
• Practice: Designing the architecture of a multi-tenant AI platform using C4 diagrams and architecture decisions as code

• Blue-Green deployment with database synchronization and zero-downtime rollback
• Canary releases with automatic rollback on SLO degradation
• Shadow deployment and parallel code execution
• Feature flags and their architecture
• Progressive delivery integrated with observability

• Rollback strategies during incidents

• Practice: Blue-Green deployment with traffic management 

• Resilience patterns: Circuit Breaker, Bulkhead, Retry, Timeout, Fallback
• Backpressure and overload management
• Event Sourcing and CQRS in production environments
• Saga patterns: orchestration and choreography
• Monolith integration and migration patterns
• Container patterns: Sidecar, Ambassador, Adapter
• Practice: Implementing Circuit Breaker and Bulkhead in an AI orchestrator with tenant isolation and automatic failover to a backup response

• API Gateway: authentication, rate limiting, and request transformation
• Service Mesh: Istio, Linkerd, and Cilium
• Protocol selection: gRPC, REST, GraphQL, WebSockets
• Contract-first design using OpenAPI and AsyncAPI
• API versioning and backward compatibility management

• Container patterns: Sidecar, Ambassador, Adapter
• Practice: Implementing Circuit Breaker and Bulkhead in an AI orchestrator with tenant isolation and automatic failover to a backup response

• GitOps at scale: managing multiple environments from a single repository
• Automated promotion of changes across environments with control gates
• Multi-cluster GitOps and cluster synchronization
• Infrastructure drift detection
• IaC at scale using Terragrunt and a module registry
• Policy-as-Code and secret management with automatic rotation
• Practice: Configuring three environments through a single repository with automated promotion, drift detection, and Slack notifications

• Zero Trust Architecture: the “Never Trust, Always Verify” principle
• SPIFFE and SPIRE: cryptographic service identity
• Mutual TLS at scale with automated certificate rotation
• Supply chain attacks: SolarWinds, XZ Utils, Log4Shell
• SLSA Level 3 and Sigstore for artifact protection
• Cloud Security Posture Management and elimination of excessive secrets
• Practice: Configuring SPIFFE-based service identity, automated certificate rotation, and supply chain attack detection using Rekor

• Threat Intelligence: data sources and indicators of compromise (IoC)
• MITRE ATT&CK for cloud and containers
• Red Team exercises and methodologies
• ATT&CK Purple Team: collaborative threat detection improvement
• Detection Engineering: writing rules for Falco and Sigma
• Incident simulations through scenario-based exercises
• Practice: Testing the resilience of your own AI platform against the MITRE ATT&CK matrix by creating Falco detection rules for each technique

• Kubernetes Operators: controller pattern and operator development
• Custom Resource Definitions (CRDs) for extending the Kubernetes API
• Platform Engineering: Internal Developer Platform and Backstage
• Advanced RBAC model and just-in-time access
• Network policies using Cilium and eBPF
• CIS Benchmark Level 2 and automated cluster security validation
• Multi-cluster management and autoscaling with KEDA
• Practice: Laboratory experiment — developing a Kubernetes Operator to manage the lifecycle of a Cloudflare Worker using Terraform and Wrangler

• SLSA Framework Levels 1–3 and practical implementation
• SBOM in production: generation, signing, and CVE tracking
• Sigstore ecosystem: Cosign, Rekor и Fulcio
• Compliance as Code: automated evidence generation for ISO 27001 and SOC 2
• Immutable audit logs and cryptographic attestations
• Detection of incompatible dependency licenses
• Practice: Building an SLSA Level 3 pipeline with artifact signing, Rekor attestation storage, and automated compliance reporting for every release

• SLO engineering: selecting metrics and building an SLO document
• Error budget policies and deployment freeze processes
• Measuring and reducing operational toil
• Blameless incident reviews: culture, process, and outcome-driven analysis
• Recovery procedures as code with automated execution
• Capacity planning and on-call management
• Practice: SLO engineering for an AI platform: creating an SLO document, Grafana error budget dashboards, and automated alerts at 50% budget consumption

• Continuous profiling in production using Pyroscope
• Distributed tracing: sampling strategies and trace-based testing
• Signal correlation using exemplars
• Performance Engineering: bottleneck analysis methodology
• FinOps: measuring service costs and tenant-based optimization
• Observability as Code: configuring dashboards and alerts via Terraform
• Practice: Adding continuous profiling to services, building tenant cost allocation dashboards, and eliminating the top 3 performance bottlenecks

• Principles of Chaos Engineering: hypotheses, blast radius, and steady state
• Experiment organization and result measurement
• Failure injection at the network, application, infrastructure, and data levels
• Automated validation of resilience patterns
• Chaos Engineering for AI systems: model degradation and slow responses
• Practice: Running five chaos experiments, measuring SLO impact, and validating correct Circuit Breaker behavior 

• OWASP LLM Top 10: Prompt Injection, Insecure Output Handling, data poisoning
• Protection against Prompt Injection in production environments
• Preventing data leakage through LLMs
• AI agent security: tool restrictions and the principle of least privilege 
• AI in DevSecOps pipelines: agents as CI/CD participants 
• LLM Gateway and centralized control of AI requests 
• Monitoring AI systems: hallucination detection and quality tracking 
• Practice: Attacking and defending an AI Worker: Prompt Injection testing, implementing protection mechanisms, configuring monitoring via Cloudflare AI Gateway, and automatic task creation upon anomaly detection

• Agentic workflows in DevSecOps without human intervention
• AI agents for incident response: automated diagnostics and remediation
• AI-assisted code review integrated with security scanners
• Generating IaC from text-based requirements
• Local models for confidential codebases
• AI tool governance: policies and audit logging
• Practice: Autonomous incident response: an AI agent analyzes logs, identifies root cause, creates a runbook change request, and notifies the on-call engineer

Yes. We cooperate with various government support programs that may provide funding for different categories of applicants, including not only unemployed individuals but also employed residents of Estonia.
Leave a request to learn which funding opportunities may be available in your case.

This course is designed for specialists with practical experience: DevOps engineers, system administrators, and developers who already work with containers, CI/CD, and cloud platforms.

For most lab assignments, a standard laptop and accounts on GitHub and Cloudflare are sufficient — both services provide free developer plans. Environment setup requirements will be clarified before the course starts.

The schedule is flexible — classes take place 2–3 times per week, and course materials remain available to you permanently. Even if you miss a class, instructors will help you catch up with the material.

You will receive an international-format certificate from the MYEDU training center, as well as a certificate from the S.E. School of Systems Analysis confirming your qualification. This document is valued by employers in the IT industry.