Alex Chen

Lead backend development for cloud-native SaaS platform serving 500K+ daily active users. Architect distributed systems and mentor junior developers.

Real-Time Analytics Engine

• Built a high-throughput event processing system handling 50M+ events daily with sub-100ms latency requirements.
• Microservices with Kafka, Redis, PostgreSQL, and Kubernetes on AWS EKS
• Designed and implemented the core ingestion pipeline, implemented exactly-once processing semantics, and established SLOs with automated alerting.
• Technical Stack: Python 3.11 FastAPI Apache Kafka Redis PostgreSQL Kubernetes Prometheus Grafana
• Challenges: Latency Reduction: Reduced p99 latency from 2.3s to 87ms through async processing and connection pooling
• Challenges: Cost Optimization: Decreased AWS infrastructure costs by 35% through intelligent batching and spot instance utilization
• Challenges: AI Integration: Implemented schema-constrained structured outputs with OpenAI API for automated data classification with 94% accuracy
• Challenges: Observability: Built comprehensive tracing with OpenTelemetry enabling 60% faster incident resolution

MCP-Integrated API Gateway

• Developed API gateway with Model Context Protocol integrations for scoped AI assistant access to internal services.
• Python, FastAPI, Redis, OAuth2, JWT, with MCP server implementations
• Designed permission-scoped MCP endpoints, implemented token-based authentication, and built regression test suites for model behavior validation.
• Technical Stack: Python FastAPI MCP Protocol OAuth2 JWT Redis pytest
• Challenges: Security Model: Implemented fine-grained permission scopes reducing unauthorized access attempts by 99.7%
• Challenges: Testing Framework: Created evaluation datasets with 500+ test cases for model output validation and regression detection

Developed data processing pipelines and REST APIs for fintech analytics platform. Collaborated with data science team on ML model deployment.

ETL Pipeline Modernization

• Replaced legacy batch processing with streaming data pipeline for real-time fraud detection.
• Python, Apache Airflow, Spark Streaming, AWS S3, Lambda, DynamoDB
• Designed DAG workflows, implemented data quality checks, and built monitoring dashboards for pipeline health.
• Technical Stack: Python 3.8 Apache Airflow PySpark AWS Lambda DynamoDB Docker
• Challenges: Throughput Improvement: Increased data throughput by 10x while reducing processing costs by 40%
• Challenges: Reliability: Achieved 99.99% pipeline uptime with automated failure recovery mechanisms

ML Model Serving Platform

• Built containerized model serving infrastructure with A/B testing and canary deployment capabilities.
• Python, Flask, Docker, Kubernetes, MLflow, Prometheus
• Developed RESTful API for model inference, implemented request batching for latency optimization, and built model versioning system.
• Technical Stack: Python Flask Docker Kubernetes MLflow Prometheus
• Challenges: Latency Optimization: Reduced average inference latency by 65% through intelligent request batching and caching