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5+ Years Experience
20+ Projects Delivered
✓ Available for new projects
Used at:
Anaqua The Virtulab Spiio Sparrow Intelligence

$ cat services.json

GCP Architecture Design

Design cloud-native infrastructure on Google Cloud Platform.

Deliverables:
  • Architecture diagrams
  • Service selection
  • Security design
  • Cost estimation
  • Migration planning

Kubernetes on GKE

Deploy and manage containerized applications on GKE.

Deliverables:
  • GKE cluster setup
  • Workload deployment
  • Autoscaling configuration
  • Ingress and networking
  • Monitoring with Cloud Monitoring

Serverless & Cloud Run

Build serverless applications with minimal operational overhead.

Deliverables:
  • Cloud Run deployments
  • Cloud Functions development
  • Event-driven architectures
  • Pub/Sub integration
  • API Gateway setup

$ man google-cloud-platform

GCP Services I Specialize In

Compute

  • GKE: Managed Kubernetes, autopilot mode
  • Cloud Run: Serverless containers
  • Cloud Functions: Event-driven serverless
  • Compute Engine: Custom VMs

Data

  • Cloud SQL: Managed PostgreSQL/MySQL
  • Firestore: NoSQL document database
  • BigQuery: Data warehouse and analytics
  • Cloud Storage: Object storage

AI/ML

  • Vertex AI: Model training and serving
  • AI Platform: Custom ML workloads
  • Cloud GPUs: GPU-accelerated compute

GCP for AI Workloads

GCP is particularly strong for AI/ML applications:

  • Vertex AI for model training and deployment
  • GKE with GPUs for custom inference
  • BigQuery ML for in-database machine learning
  • Cloud Run for scalable API endpoints
  • Pub/Sub for async AI processing

$ cat README.md

GCP Architecture Patterns

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# GKE Deployment Configuration
apiVersion: apps/v1
kind: Deployment
metadata:
  name: ai-service
spec:
  replicas: 3
  template:
    spec:
      containers:
      - name: ai-service
        image: gcr.io/project/ai-service:v1.0
        resources:
          requests:
            memory: "1Gi"
            cpu: "500m"
          limits:
            memory: "2Gi"
            cpu: "1000m"
        env:
        - name: DATABASE_URL
          valueFrom:
            secretKeyRef:
              name: cloudsql-credentials
              key: connection_string
      # Cloud SQL Proxy sidecar
      - name: cloud-sql-proxy
        image: gcr.io/cloudsql-docker/gce-proxy:1.28.0
        command:
          - "/cloud_sql_proxy"
          - "-instances=project:region:instance=tcp:5432"

GCP Services Comparison

Use CaseGCP ServiceAlternative
ContainersGKE (full control)Cloud Run (serverless)
FunctionsCloud FunctionsCloud Run (containers)
DatabasesCloud SQLFirestore (NoSQL), Spanner (global)
QueuesPub/SubCloud Tasks
StorageCloud StorageFilestore (NFS)
AnalyticsBigQueryDataproc (Hadoop)

When to Choose GCP

GCP excels for:

  • Kubernetes: GKE is industry-leading
  • Data/Analytics: BigQuery is unmatched
  • AI/ML: Vertex AI, TPUs, GPU availability
  • Global Scale: Google’s network infrastructure
  • Container-Native: Cloud Run simplifies serverless

Experience:

Case Studies: Real-time EdTech Platform

Related Technologies: Docker/Kubernetes, Python, PostgreSQL, FastAPI

$ ls -la projects/

Enterprise AI Platform

@ Anaqua (RightHub)
Challenge:

Deploy AI microservices with varying compute requirements including GPU workloads.

Solution:

GKE with node pools for CPU and GPU workloads, Cloud SQL for data, Cloud Storage for documents, and Pub/Sub for async processing.

Result:

99.9% uptime, elastic scaling for AI inference.

Real-Time EdTech Platform

@ The Virtulab
Challenge:

Deploy scalable backend for video education with real-time features.

Solution:

GKE for microservices, Cloud Build for CI/CD, Firestore for real-time data, and Cloud Functions for events.

Result:

Scalable platform supporting concurrent video sessions.

IoT Analytics Platform

@ Spiio
Challenge:

Process time-series data from agricultural sensors with analytics capabilities.

Solution:

GKE for data ingestion services, BigQuery for analytics, and Cloud Functions for alerting.

Result:

Real-time analytics on 40,000+ hourly sensor readings.

$ diff me competitors/

+ 5+ years of production GCP experience
+ GKE specialist—complex Kubernetes deployments
+ AI workload expertise—GPU scheduling, model serving
+ Cost optimization—right-sizing, committed use discounts
+ Multi-cloud experience—can work with AWS/GCP hybrid

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