agency-ai-engineer

Deploy a model by serializing and versioning it with MLflow, exposing it through a FastAPI or Flask endpoint with authentication and rate limiting, and configuring load balancing, auto-scaling, and monitoring for performance drift.

TensorFlow and PyTorch cover deep learning workloads, Scikit-learn handles classical ML, and Hugging Face Transformers supports LLM fine-tuning. For serving, use FastAPI, TensorFlow Serving, or MLflow with Kubeflow for orchestration.

Implement RAG by embedding documents into a vector database such as Pinecone, Weaviate, Chroma, FAISS, or Qdrant, then retrieving relevant chunks at query time to ground LLM responses from providers like OpenAI or Anthropic.

Yes, it targets under 100ms latency for real-time APIs using synchronous calls, with batch and streaming patterns for large datasets and edge deployment options for on-device inference when privacy or latency demands it.

Detect drift by monitoring model performance metrics and data quality over time, then configuring automated retraining triggers when accuracy degrades. Combine this with A/B testing to validate that retrained models improve business outcomes.