Programming foundations
Design scalable AI-backed systems
Reason about latency, queues, caching, storage and failure modes for systems that call models.
~30 focused hours·advanced
Tools: load balancers, message queues (Kafka/SQS), caching (Redis), vector DBs, rate limiting
Market relevance — share of job ads asking for this
What employers mean
You should be able to…
- Design a system that serves an LLM API to thousands of concurrent users without falling over
- Choose between synchronous and queue-based (async) processing for a slow AI task
- Add caching to avoid re-calling an expensive model for repeated inputs
- Design for a model provider outage (fallback model, retries with backoff, circuit breaker)
- Estimate capacity: how many GPUs/requests-per-second does a given traffic pattern need
- Design rate limiting and quota enforcement per tenant/customer
- Reason about data flow for a RAG pipeline (ingestion, embedding, retrieval, generation) at scale
Needs first: Build and consume REST APIs, Deploy an AI service to the cloud
Learn — free, link-checked
The few resources that matter
Read · intermediate · 30 min · 12factor.net
The Twelve-Factor App
The config/state/logging principles behind every scalable cloud service, including the ones you'll deploy for AI inference. — Heroku / community
Read · intermediate · 40 min · freecodecamp.org
Systems Design for Interviews
A faster on-ramp than the primer repo — frames the same trade-offs (latency, consistency, failure) as a worked interview answer. — freeCodeCamp
Build from · advanced · 180 min · github.com
System Design Primer
The most-starred open reference for system design interviews — covers caching, queues and scaling patterns you'll reuse when designing AI-backed systems. — donnemartin (GitHub)
Practice
Resilient RAG API for NBFC Loan Documents
Design and partially build (or diagram + prototype critical paths of) a RAG API that answers questions over a growing corpus of NBFC loan/KYC documents: an ingestion queue, an embedding+vector-store step, a caching layer for repeated questions, and a fallback path when the primary LLM provider errors out. Document capacity estimates and failure-mode handling in an architecture doc.
Done when
- Architecture diagram covering ingestion, retrieval, generation, caching and failure fallback
- Written capacity estimate (requests/sec, storage growth, cost) with assumptions stated
- A working prototype of at least the caching layer or the fallback/retry logic, with tests
- A documented failure scenario (provider outage or queue backlog) and how the design handles it
Prove it
Evidence a recruiter can check
- Architecture diagram + design doc published in a GitHub repo or as a shared doc
- A working prototype of the highest-risk component (cache, queue, or fallback logic) with tests
- A load-test or back-of-envelope capacity calculation with numbers, not just prose
Interview
Questions you'll get asked
- Design a chatbot backend that needs to serve 10,000 concurrent users with sub-2s latency.
- How would you cache LLM responses without serving stale or unsafe answers?
- Design a system that ingests 1M documents a day into a RAG pipeline.
- Your model provider's API is down for 10 minutes — how does your system degrade gracefully?
- How would you rate-limit an API per customer while keeping p99 latency low?
- Walk me through the trade-offs of a synchronous vs async (queue + webhook) API for a slow AI task.
- How would you scale a vector search index from 1M to 100M embeddings?