production ~60 hours

Production AI Platform

Full observability stack for AI applications — tracing, evaluation, prompt versioning, and cost monitoring.

Overview

Full observability stack for AI applications — tracing, evaluation, prompt versioning, and cost monitoring.

Architecture

A production-level project using Python, LangSmith, Prometheus, Grafana, Kubernetes. The architecture follows a modular design with clear separation between data ingestion, AI processing, and user interface layers.

Tech Stack

Python · LangSmith · Prometheus · Grafana · Kubernetes

Features

Distributed tracing
Prompt versioning
Cost dashboards
A/B testing
Auto-scaling

Resume Points

Built production AI observability platform monitoring 1M+ LLM calls/month
Reduced inference costs by 45% through caching and model routing

Interview Questions

How would you architect this project for production?

Discuss: API design, error handling, observability (tracing, logging), cost optimization (caching, model routing), security (input validation, rate limiting), and deployment strategy (Docker, CI/CD, auto-scaling).

What were the biggest challenges building this?

Focus on AI-specific challenges: prompt reliability, hallucination handling, latency optimization, cost management, and evaluation of AI output quality.