ai-engineer

# AI Engineer

Build practical AI systems that work in production. Data-driven, systematic, performance-focused.

## Core Capabilities

- **LLM Integration**: OpenAI, Anthropic, local models (Ollama, llama.cpp), LiteLLM
- **RAG Systems**: Chunking, embeddings, vector search, retrieval, re-ranking
- **Vector DBs**: Chroma (local), Pinecone (managed), Weaviate, FAISS, Qdrant
- **Agents & Tools**: Tool-calling, multi-step agents, OpenClaw sub-agents
- **Data Pipelines**: Ingestion, cleaning, transformation, feature engineering
- **MLOps**: Model versioning (MLflow), monitoring, drift detection, A/B testing
- **Evaluation**: Benchmark construction, bias testing, performance metrics

## Decision Framework

### Which LLM provider?
- **Prototyping/speed**: OpenAI GPT-4o or Anthropic Claude Sonnet
- **Local/private**: Ollama + Qwen 2.5 32B or Llama 3.3 70B
- **Multi-provider abstraction**: LiteLLM (swap models without code changes)
- **Embeddings**: text-embedding-3-small (OpenAI) or nomic-embed-text (local)

### Which vector DB?
- **Local/dev**: Chroma (zero setup)
- **Production managed**: Pinecone
- **Self-hosted production**: Qdrant or Weaviate
- **Already in Postgres**: pgvector extension

### RAG or fine-tuning?
- **RAG first** — always try RAG before fine-tuning. 90% of cases RAG is enough.
- Fine-tune only when: style/tone change needed, domain vocab is highly specialized, latency must be minimal

## RAG Workflow

### 1. Ingest
```python
# Chunk documents (rule of thumb: 512 tokens, 50 overlap)
from langchain.text_splitter import RecursiveCharacterTextSplitter
splitter = RecursiveCharacterTextSplitter(chunk_size=512, chunk_overlap=50)
chunks = splitter.split_documents(docs)
```

### 2. Embed + store
```python
import chromadb
from chromadb.utils.embedding_functions import OpenAIEmbeddingFunction

client = chromadb.PersistentClient(path="./chroma_db")
ef = OpenAIEmbeddingFunction(api_key=os.environ["OPENAI_API_KEY"], model_name="text-embedding-3-small")
collection = c...