Production-ready AI API service with prompt engineering, RAG readiness, caching, monitoring, and secure deployment.
This repository showcases a production-ready AI API service built with TypeScript, designed to demonstrate everything expected from a Senior AI Integration Engineer. It currently includes:
- AI model integration and prompt engineering
- Structured input/output validation
- Resilience via retry/backoff logic
- Cost optimization through caching
- Containerized deployment workflows
Below are the features currently implemented and the ones in active or planned development.
- Role-based few-shot prompts with structured JSON outputs
- API wrapper around OpenAI (and other LLMs)
- Ensures consistent, schema-compliant responses
- Input validation to enforce correct request schemas
- Preprocessing for data normalization and sanitation
- Ready for extension to support complex pipelines (PDFs, logs, etc.)
- Retry mechanism with exponential backoff for transient errors
- Redis-based caching to reduce redundant AI calls
- Dockerized for deployment in scalable environments (e.g., Kubernetes)
- Basic response guardrails against invalid or unsafe outputs
- Extension-ready for:
- PII redaction (via regex or NER)
- Privacy regulation compliance (GDPR, HIPAA)
- Content moderation integration (OpenAI Moderation API, toxicity filters)
- Logging of requests, responses, and error events
- Cost optimization via caching and payload truncation
- Designed for expansion to:
- Token-level cost tracking
- Dashboard integration (Prometheus, Grafana)
- Alerting for high cost or error spikes
- Built using FastAPI/Node.js for efficient microservice architecture
- Docker-enabled for consistent staging & production environments
- Prepared for CI/CD workflows with integration testing
| Feature Area | Status |
|---|---|
| Multi-instance Scaling | In Process (Kubernetes) |
| RAG Pipeline + Vector DB | In Process (Pinecone/Weaviate) |
| Message Queues & DLQ | In Process (RabbitMQ/Kafka) |
| PII Redaction | Implementing |
| Moderation & Compliance | Implementing |
| Cost Dashboards & Alerts | In process (Prometheus etc.) |
This project embodies core responsibilities of an AI Integration Engineer—covering prompt design, model calls, reliability, cost-efficiency, security, and deployment best practices. As additions like vector DBs, RAG workflows, and monitoring roll out, this repository will become a near-perfect showcase of AI integration engineering at scale.
git clone git@github.com:ManibalaSinha/ai-api.git
cd ai-api
npm install
npm run dev # Launch local development server
npm run build # Build for production
docker build -t ai-api .