IRL & Online Program
AI Product Engineering
Take LLMs from prototype to production. Optimize for speed, manage API costs, and build scalable full-stack AI applications.
Hands-on Highlights
- Optimize LLM inference latency with vLLM
- Implement semantic caching with Redis
- Set up observability using LangSmith
- Build a production-ready Next.js + FastAPI SaaS
Detailed Syllabus
Week 1-2
Full-Stack AI Architecture
- Decoupling frontend and backend for AI workloads
- Setting up FastAPI for Python ML microservices
- Next.js API routes and server actions
- Handling streaming responses (Server-Sent Events)
Week 3-5
Inference Optimization
- Understanding KV Cache and token generation speed
- Deploying open-source models with vLLM
- Batching requests for high throughput
- TensorRT-LLM basics for extreme performance
Week 6-8
Caching & Cost Management
- Implementing Redis for exact-match caching
- Vector-based semantic caching (GPTCache)
- Tokenomics and prompt compression strategies
- Rate limiting and abuse prevention
Week 9-10
Observability & Analytics
- Setting up Helicone or LangSmith for tracing
- Evaluating prompt drift in production
- A/B testing LLM outputs
- Capstone: Shipping a scalable AI SaaS tool
Target Roles & Career Paths
Full-Stack AI Engineer
AI Product Developer
Backend Engineer (AI)
These are the primary roles you will be equipped to apply for upon successful completion of the course and portfolio projects.