Engineering decisions, trade-offs, and system thinking behind the work.
Distributed SystemsEngineering Case Study
Distributed Job Processing Platform
Built a resilient async processing system for report generation and content tasks that needed queue-based orchestration, retry safety, and scalable worker execution.
Challenge
The bottleneck was not raw compute — it was coordinating long-running workloads across multiple services without losing reliability under failure, retries, and bursts of demand.
Solution
I designed a producer-consumer architecture using Redis-backed queues, background workers, and durable storage for task metadata. The services were decoupled so the API layer could respond fast while processing happened asynchronously and recoverably.
Impact
This improved throughput, isolated failures between services, and made the system resilient to spikes without degrading user-facing latency.
Key points
- Queue-based async orchestration
- Retry-safe task execution
- Failure isolation across services
- Scalable worker fan-out
Node.jsRedisDockerBullMQPostgreSQLAWS S3
Cloud & ArchitectureEngineering Case Study
Content Processing Microservices
Designed a content-processing workflow that accepted large file and text workloads and pushed them through multiple stages with API gateway and event-driven coordination.
Challenge
The main issue was maintaining clean service boundaries while enabling real-time updates, identity enforcement, and high-throughput processing without a monolithic bottleneck.
Solution
I separated Gateway, Identity, Processing, and Event-driven components, and introduced SSE-based progress visibility so the frontend could track long-running jobs without constant polling.
Impact
The design made the platform easier to extend, safer to operate, and better suited for asynchronous content workflows at scale.
Key points
- API gateway protection
- OAuth-based identity flow
- SSE real-time status updates
- Event-driven orchestration
TypeScriptNestJSKeycloakKafka-like patternsRedisPostgreSQL
AI EngineeringEngineering Case Study
AI Workflow & Evaluation Thinking
Focused on building AI-first product patterns where quality, observability, and evaluation matter as much as model choice itself.
Challenge
AI systems often look promising in demos but fail in production because they lack structured evaluation, guardrails, and reliable feedback loops.
Solution
The approach is grounded in system design fundamentals: model selection, prompt architecture, observability, and evaluation loops that help detect quality drift over time.
Impact
This creates a stronger engineering mindset around trustworthy AI products rather than shipping output without measurement or failure handling.
Key points
- Prompt architecture
- Evaluation-first design
- Observability for AI workflows
- Guardrails and reliability
LLM AppsRAGEvaluationPrompt DesignPythonAI Ops