What does AI and machine-learning engineering involve?

Applied AI engineering turns models into reliable products.

• LLM applications — prompt design, structured outputs, tool use, and retrieval-augmented generation (RAG) with embeddings and a vector or keyword index to ground answers in your own data
• Evaluation — automated test sets, regression checks and human review; you cannot improve what you do not measure
• MLOps — versioned data and models, reproducible training, deployment, monitoring for drift and rollback
• Reliability and cost — caching, fallbacks, timeouts, rate limits, token budgets and latency monitoring
• Safety and security — prompt-injection defences, least-privilege tools, PII handling and human approval for risky actions
• Governance — document intended use, limitations and data provenance

Choose the simplest approach that meets the need: a rules engine or search index often beats a model.