Dmitry Zhuravlev — Cloud DevOps, Platform Engineering & AI Infrastructure Portfolio
Production-grade cloud platforms, AI infrastructure, reliability-focused systems, and secure delivery solutions built around operational challenges.
Focus Areas: AI Infrastructure • Distributed Agent Systems • DevSecOps • Platform Engineering • SRE • Cloud Architecture • Kubernetes • CI/CD • FinOps • Infrastructure as Code • Observability • Automation
Featured Platform Projects
1. AI Infrastructure & Operations Platform
AI operations platform designed to coordinate domain-specific agents across SRE, CI/CD, secure delivery, FinOps, and cloud operations.
Core Components:
- AI agents — domain-specific operational agents for diagnostics, delivery, cost analysis, and infrastructure workflows
- Agent orchestration — coordination layer for multi-agent execution across connected engineering systems
- Python — platform services, integrations, and automation components
- OpenTelemetry — telemetry collection and tracing across agent workflows and platform services
- Cloud Run and Compute Engine — hybrid execution model for stateless services and stateful agent workloads
- Terraform — modular provisioning of shared platform infrastructure and isolated agent environments
What this project delivers:
- Unified control plane for AI-assisted cloud operations
- Distributed agent architecture spanning multiple engineering domains
- Integration path for SRE, CI/CD, FinOps, and secure delivery systems
- Governance boundaries for safe, observable, and progressively enabled agent actions
2. Continuous Integration Build Platform
Scalable build platform based on ephemeral self-hosted runners for cloud CI workloads.
Core Components:
- GitHub Actions — orchestration of CI workflows and job scheduling
- Compute Engine — ephemeral runners providing isolated execution environments
- Cloud Run — control plane service managing runner lifecycle and scaling decisions
- Terraform — dynamic provisioning and teardown of compute resources
- Docker — standardized build environment and artifact packaging
- Container Registry — storage and distribution of build artifacts
- Cloud Logging & Monitoring — visibility into runner execution, failures, and system behavior
What this project delivers:
- On-demand provisioning of isolated CI runners
- Scalable parallel build execution
- Automated lifecycle management of build infrastructure
- Platform-style abstraction over CI workloads
3. SRE & Reliability Engineering Platform
GitOps-based platform implementing SLO- and error budget–driven release governance for Kubernetes workloads.
Core Components:
- Kubernetes (GKE) — multi-environment cluster setup with namespace isolation and autoscaling for workload segmentation
- Argo CD — GitOps control plane implementing the app-of-apps pattern and environment-based deployment promotion
- Argo Rollouts — canary deployment strategy with analysis templates driven by Prometheus metrics
- Prometheus — metrics collection and multi-window SLO evaluation
- Grafana — visualization of SLOs, error budgets, and live rollout health signals
- Helm — reusable and environment-specific configuration management for microservice deployment
- Terraform — modular infrastructure provisioning for clusters, networking, and platform components
What this project delivers:
- Progressive delivery controlled by observability signals
- SLO- and error budget–driven deployment decisions
- Automated promotion and rollback strategies
- Platform-level control over service reliability and release safety
4. Secure Delivery Platform
Secure cloud-native delivery platform focused on trusted builds, policy enforcement, and Kubernetes-native deployment controls.
Core Components:
- Cloud Build — reproducible and isolated build pipeline for trusted artifact creation
- Artifact Registry — immutable artifact storage with controlled access
- Binary Authorization — policy-based deployment validation ensuring only trusted images are deployed
- Cloud Deploy — progressive delivery with controlled rollout strategies
- Kubernetes (GKE) — controlled runtime environment with enforced deployment policies
- Secret Manager — centralized secret storage with secure injection into workloads
- Terraform — standardized infrastructure provisioning using reusable and customized Google Cloud modules
- Cloud Logging & Monitoring — audit trail, deployment visibility, and operational signals
What this project delivers:
- Controlled software delivery pipeline with integrated policy validation
- Deployment verification and enforcement mechanisms
- Trusted artifact lifecycle from build to runtime
- Security integrated into the delivery workflow
5. Enterprise Cloud Migration
Migration and modernization project focused on architecture, delivery automation, and cloud operating models.
Core Components:
Compute & Runtime
- Compute Engine + Managed Instance Groups — scalable and resilient application hosting layer
- Cloud Run — containerized services for flexible workload execution
Networking
- Custom VPC with private subnets — isolated network environment
- No public IPs on instances — reduced attack surface
- Cloud NAT — controlled outbound connectivity
- HTTPS Load Balancer — secure entry point with TLS termination
Identity & Access
- Dedicated service accounts — workload-level identity isolation
- Granular IAM roles — least-privilege access to Firestore and Cloud Storage
Data & Storage
- Firestore (Datastore mode) — managed NoSQL database
- Cloud Storage — object storage for application data
Infrastructure & Delivery
- Terraform — infrastructure as code with reproducible environments
- GitHub Actions — automated build and deployment workflows
What this project delivers:
- Migration from traditional infrastructure to cloud-native architecture
- Private networking model with controlled access patterns
- Integration of infrastructure, identity, and application delivery
- Multi-layer system design across compute, networking, and CI/CD
6. FinOps & Cloud Cost Assessment
Advanced cloud assessment platform for identifying waste, evaluating optimization opportunities, and producing structured FinOps findings across Google Cloud environments.
Core Components:
- Python — modular assessment engine separating resource discovery, normalization, evaluation, and reporting
- Google Cloud APIs — structured discovery of projects, resources, utilization signals, and billing-relevant metadata
- Cloud Run — stateless execution layer for repeatable assessment workloads
- Cloud Scheduler — scheduled assessment cycles for recurring cost visibility
- Terraform — modular provisioning of assessment infrastructure and supporting cloud resources
- Reporting pipeline — structured transformation of collected facts into findings and recommendations
What this project delivers:
- Multi-project cloud cost and resource assessment
- Deterministic fact-to-finding evaluation workflow
- Prioritized optimization recommendations with supporting evidence
- Repeatable assessment structure suitable for client delivery and governance reviews
Open-source note:
Selected components, architecture documentation, and implementation patterns are published publicly while protected commercial logic remains separate. The earlier Cloud Optimization Engine represents the open-source foundation from which this platform evolved.
Engineering Approach
All flagship projects follow consistent engineering principles:
- Architecture-first design with clear system boundaries and responsibilities
- Integrated security controls across identity, infrastructure, AI agents, and delivery workflows
- Cost-aware infrastructure decisions and resource modeling
- Structured documentation covering architecture, deployment, security, operations, and cost analysis
- Troubleshooting guides and operational scenarios captured as part of implementation
- Modular infrastructure and reusable configuration patterns
- Infrastructure as Code as the default provisioning model
Supporting Work
A curated collection of supporting implementations, AI infrastructure projects, experiments, and reusable engineering patterns complementing the flagship platform projects.
Includes:
- AI infrastructure and distributed agent systems
- CI/CD implementations (GitHub Actions, Cloud Build, Jenkins)
- Multi-cloud infrastructure examples (AWS, Azure, GCP)
- Kubernetes patterns (Ingress, Istio, Kustomize, OpenShift)
- GitOps workflows (Argo CD, Helm)
- Observability setups (OpenTelemetry, Prometheus, Grafana, Datadog)
- Infrastructure modules (Terraform, Ansible)
- Automation scripts (Python, Bash)