Cloud-Native Microservices Platform

Overview

Architected and developed a cloud-native platform for processing financial transactions at enterprise scale. The system handles millions of daily transactions across multiple cloud providers while maintaining strict compliance and security requirements.

Architecture

The platform follows a domain-driven design with clear bounded contexts:

┌─────────────────────────────────────────────────────────────┐
│                      API Gateway (Apigee)                    │
└─────────────────────────┬───────────────────────────────────┘
                          │
    ┌─────────────────────┼─────────────────────┐
    │                     │                     │
    ▼                     ▼                     ▼
┌─────────┐         ┌─────────┐          ┌─────────┐
│ Payment │         │ Account │          │  Fraud  │
│ Service │         │ Service │          │Detection│
└────┬────┘         └────┬────┘          └────┬────┘
     │                   │                    │
     └───────────────────┼────────────────────┘
                         │
              ┌──────────┴──────────┐
              │   Message Broker    │
              │   (Kafka/Pub-Sub)   │
              └─────────────────────┘

Tech Stack

Backend Services (Java/Spring Boot)

Each microservice follows hexagonal architecture:

@Service
@Transactional
public class PaymentProcessor {
 
    private final PaymentRepository repository;
    private final FraudDetectionClient fraudClient;
    private final EventPublisher eventPublisher;
 
    public PaymentResult process(PaymentRequest request) {
        // Validate request
        ValidationResult validation = validate(request);
        if (!validation.isValid()) {
            return PaymentResult.rejected(validation.errors());
        }
 
        // Check for fraud
        FraudScore score = fraudClient.analyze(request);
        if (score.isHighRisk()) {
            eventPublisher.publish(new FraudAlertEvent(request, score));
            return PaymentResult.pendingReview();
        }
 
        // Process payment
        Payment payment = Payment.create(request);
        repository.save(payment);
 
        eventPublisher.publish(new PaymentProcessedEvent(payment));
        return PaymentResult.success(payment.getId());
    }
}

AI-Powered Fraud Detection (Python)

Integrated machine learning models for real-time fraud detection:

class FraudDetector:
    def __init__(self, model_path: str):
        self.model = load_model(model_path)
        self.feature_extractor = FeatureExtractor()
 
    def analyze(self, transaction: Transaction) -> FraudScore:
        features = self.feature_extractor.extract(transaction)
        probability = self.model.predict_proba(features)[0][1]
 
        return FraudScore(
            probability=probability,
            risk_level=self._classify_risk(probability),
            factors=self._explain_prediction(features)
        )

Infrastructure (Kubernetes)

Deployed on multi-cloud Kubernetes clusters:

apiVersion: apps/v1
kind: Deployment
metadata:
  name: payment-service
spec:
  replicas: 3
  strategy:
    type: RollingUpdate
    rollingUpdate:
      maxSurge: 1
      maxUnavailable: 0
  template:
    spec:
      containers:
        - name: payment-service
          image: payment-service:latest
          resources:
            requests:
              memory: "512Mi"
              cpu: "500m"
            limits:
              memory: "1Gi"
              cpu: "1000m"
          livenessProbe:
            httpGet:
              path: /actuator/health/liveness
              port: 8080
          readinessProbe:
            httpGet:
              path: /actuator/health/readiness
              port: 8080

Observability

Comprehensive monitoring stack using Splunk and custom dashboards:

• Distributed tracing with correlation IDs across services
• Real-time alerting on SLA breaches
• Business metrics alongside technical metrics
• Automated incident response playbooks

Results

• 99.9% uptime maintained over 18 months
• 40% reduction in incident response time through improved observability
• Millions of transactions processed daily with sub-second latency
• Multi-cloud resilience - automatic failover between AWS and GCP

Key Learnings

Building distributed systems at scale requires embracing failure as a constant:

1. Design for failure - Circuit breakers, retries, and graceful degradation
2. Observability first - You can't fix what you can't see
3. Automate everything - Manual processes don't scale
4. Start simple - Microservices add complexity; earn that complexity