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🐳 Docker & Kubernetes

Containerized deployments that scale reliably

6+ Years Experience
30+ Projects Delivered
✓ Available for new projects
Used at:
Anaqua Flowrite The Virtulab Spiio OPERR Technologies Drop Delivery

$ cat services.json

Containerization Strategy

Containerize applications for consistent, reproducible deployments.

Deliverables:
  • Dockerfile optimization
  • Multi-stage builds
  • Base image selection
  • Security scanning
  • Registry setup

Kubernetes Deployment

Design and implement Kubernetes infrastructure for production workloads.

Deliverables:
  • Cluster architecture design
  • Manifest/Helm chart development
  • Ingress and networking
  • Secrets management
  • Monitoring and logging

CI/CD Pipeline Integration

Integrate container workflows into your development pipeline.

Deliverables:
  • GitHub Actions / GitLab CI setup
  • Automated testing in containers
  • Image building and pushing
  • Deployment automation
  • Rollback strategies

$ man docker-kubernetes

My Container Philosophy

Containers aren’t just about packaging—they’re about reliability, reproducibility, and scale.

My approach:

  • Immutable infrastructure: Build once, deploy anywhere
  • 12-factor app principles: Configuration through environment
  • Security first: Minimal base images, non-root users
  • Observability: Built-in health checks, metrics, logging

Kubernetes Patterns I Implement

Deployment Strategies

  • Rolling updates with health checks
  • Blue-green deployments
  • Canary releases

Scaling

  • Horizontal Pod Autoscaling
  • Cluster autoscaling
  • Resource quotas and limits

Reliability

  • Pod Disruption Budgets
  • Liveness and readiness probes
  • Anti-affinity rules

$ cat README.md

Why Containers Matter

Containers solve the “works on my machine” problem definitively:

Without ContainersWith Containers
Environment driftIdentical everywhere
Manual server setupInfrastructure as code
“Big bang” deploysGradual rollouts
Scaling is painfulHorizontal scaling
Resource wasteEfficient packing

My Container Stack

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# Optimized Multi-Stage Build
FROM python:3.11-slim as builder
WORKDIR /app

# Install dependencies first (cache layer)
COPY requirements.txt .
RUN pip wheel --no-cache-dir --wheel-dir /wheels -r requirements.txt

FROM python:3.11-slim as runtime
WORKDIR /app

# Security: Non-root user
RUN useradd --create-home appuser
USER appuser

# Install pre-built wheels
COPY --from=builder /wheels /wheels
RUN pip install --no-cache-dir /wheels/*

# Application code
COPY --chown=appuser:appuser . .

# Health check
HEALTHCHECK --interval=30s --timeout=3s \
  CMD curl -f http://localhost:8000/health || exit 1

EXPOSE 8000
CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000"]

Kubernetes Manifests I Write

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# Production-Ready Deployment
apiVersion: apps/v1
kind: Deployment
metadata:
  name: ai-service
spec:
  replicas: 3
  strategy:
    type: RollingUpdate
    rollingUpdate:
      maxSurge: 1
      maxUnavailable: 0
  template:
    spec:
      containers:
      - name: ai-service
        image: registry/ai-service:v1.2.3
        resources:
          requests:
            memory: "512Mi"
            cpu: "250m"
          limits:
            memory: "1Gi"
            cpu: "1000m"
        livenessProbe:
          httpGet:
            path: /health
            port: 8000
          initialDelaySeconds: 10
          periodSeconds: 30
        readinessProbe:
          httpGet:
            path: /ready
            port: 8000
          initialDelaySeconds: 5
          periodSeconds: 10
        env:
        - name: DATABASE_URL
          valueFrom:
            secretKeyRef:
              name: db-credentials
              key: url

Tools in My Container Toolbox

  • Container Runtime: Docker, containerd
  • Orchestration: Kubernetes, Nomad, Docker Swarm
  • Package Management: Helm, Kustomize
  • Service Mesh: Istio basics
  • Monitoring: Prometheus, Grafana, Loki
  • CI/CD: GitHub Actions, GitLab CI, ArgoCD
  • Cloud: AWS EKS, GCP GKE, DigitalOcean K8s

Experience:

Case Studies: Real-time EdTech Platform | IoT Agriculture Data Pipeline

Related Technologies: AWS, GCP, Microservices, Python

$ ls -la projects/

AI Platform Infrastructure

@ Anaqua (RightHub)
Challenge:

Deploy and scale multiple AI microservices with varying resource requirements.

Solution:

Kubernetes on GKE with custom resource requests, GPU node pools for ML workloads, and HPA for auto-scaling.

Result:

99.9% uptime, elastic scaling for unpredictable AI workloads.

Multi-Region IoT Platform

@ The Virtulab
Challenge:

Deploy real-time streaming infrastructure with low-latency requirements.

Solution:

GKE deployment with regional clusters, WebRTC services, and real-time data pipelines in containers.

Result:

Scalable EdTech platform handling concurrent video sessions.

LLM API Services

@ Flowrite
Challenge:

Deploy LLM-powered services with consistent environments across dev, staging, and production.

Solution:

Docker-based development workflow, Nomad for production (later K8s), with Nix for reproducibility.

Result:

Zero configuration drift, reliable deployments during rapid growth.

$ diff me competitors/

+ 6+ years of production container experience
+ Both AWS EKS and GCP GKE expertise
+ Security-conscious—minimal images, RBAC, secrets management
+ Full-stack DevOps—CI/CD, monitoring, incident response
+ AI/ML workload experience—GPU scheduling, model serving

Containerize Your Application

Within 24 hours

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