Browse AI Skills

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9 skills

Architect scalable AI platforms — model serving, MLOps pipelines, multi-model systems, cost governance, and enterprise integration.

Master the full stack of AI engineering — from model selection and prompt architecture to deployment pipelines and observability.

Deep expertise in large language model internals — training, fine-tuning, RLHF, quantization, and scalable inference serving.

Build reliable, observable AI agents using tool use, memory systems, multi-agent orchestration, and safety guardrails.

Master systematic prompt design — chain-of-thought, few-shot patterns, structured output, evaluation frameworks, and iterative refinement.

Design retrieval-augmented generation pipelines — from chunking and embedding strategies to hybrid search and re-ranking.

Own the full ML lifecycle — experiment tracking, model registry, CI/CD for models, monitoring, drift detection, and rollback strategies.

Define and deliver AI-native products — opportunity discovery, capability mapping, user research for AI features, and outcome measurement.

Harden AI systems against prompt injection, jailbreaks, model extraction, data poisoning, and supply chain attacks.