AI System Architecture Design
Architectural mistakes at early stages are the most costly. Wrong approach choice (ML vs. LLM vs. rule-based), ignoring latency requirements, absence of data pipeline — all discovered in production. We design AI architectures that scale and maintain.
Architectural Design Components
AI Strategy: First question — do we need AI at all. For each functional area: what ML/AI provides vs. deterministic algorithm, expected business metric improvement, cost of model errors.
Data Architecture:
- Data sources and collection pipelines
- Feature Store (Feast, Tecton, Hopsworks) for feature reuse
- Data versioning (Delta Lake, LakeHouse vs. traditional DWH)
- Labeling pipeline for supervised tasks (Label Studio, Scale AI)
- Data quality monitoring (Great Expectations)
Model Architecture:
- Monolith vs. ensemble vs. multi-level system
- Online vs. offline inference (or hybrid)
- Single model vs. multi-model orchestration
- LLM vs. fine-tuned smaller model vs. traditional ML — for each task
Serving Architecture:
- Synchronous (REST/gRPC) vs. Asynchronous (queue-based) inference
- Batch inference for analytical tasks
- Streaming inference (Kafka + Flink) for real-time tasks
- Caching strategy (semantic caching for LLM, TTL for stable predictions)
MLOps Foundation:
- Experiment tracking (MLflow, W&B)
- Model Registry with staging/production environments
- CI/CD for ML (data tests, model smoke tests)
- Monitoring: data drift, model performance, system metrics
Typical Architectural Patterns
RAG (Retrieval-Augmented Generation): Optimal for corporate chatbots, knowledge base QA, document analysis. Components: document ingestion pipeline, vector store (Qdrant/Weaviate), LLM + reranker.
Multi-Stage Pipeline: Retrieval → Filtering → Scoring → Ranking. Each stage independently scales and replaces. Application: recommendation systems, search.
Agentic Architecture: LLM + tool use + memory + planning. LangGraph / AutoGen for complex multi-step tasks. Requires careful guardrails and fallback logic design.
Feature Store + Online ML: Actual features computed in real-time (Flink/Kafka) and stored in Redis. Model makes prediction on fresh features. Application: fraud detection, dynamic pricing.
Documentation
Design output: Architecture Decision Records (ADR), component diagram, data flow diagram, capacity plan (compute + storage + cost), implementation roadmap by priorities.
Timeline
Discovery + Architecture Design: 2–4 weeks depending on system complexity.