Glossary

AI Systems Engineer

What is AI Systems Engineer?

An AI systems engineer designs and runs AI applications as a complete system: connecting models with data sources, interfaces and existing software, ensuring security, monitoring and cost control, and moving prototypes into stable operation. Unlike pure model development, the focus is on integration. One example is connecting a support agent to the ticketing system, including logging and access rights.

How KI.ordo helps

Through its “AI development” service, KI.ordo takes on integration tasks of this kind for clients.

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Topics

Role
Integration
Operations

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Related terms

Machine learning pipeline

A machine learning pipeline is a defined sequence of steps that turns raw data into a deployable model: collecting and cleaning data, preparing features, training, evaluating, deploying and monitoring. Automating these steps makes results reproducible and lets new data be processed with little effort. A retailer, for instance, can retrain and test its sales forecasting model every week using the latest figures.

MLOps
automation
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Model Context Protocol (MCP)

The Model Context Protocol (MCP) is an open standard that lets AI applications access external tools, data sources and services in a uniform way. An MCP server provides functions, and an MCP client, such as an AI assistant, can call them. Anthropic introduced MCP in November 2024; since December 2025 it has been developed under the Agentic AI Foundation of the Linux Foundation (as of October 2026). This avoids building every integration separately.

interface
standard
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AI Automation System

An AI automation system combines traditional process automation with AI capabilities such as understanding text, classification or decision support. This makes it possible to automate workflows with unstructured inputs too, such as emails, forms or enquiries. A typical setup is a chain of trigger, processing steps, conditions and output, with human approval at critical points. Example: incoming enquiries are automatically categorised, summarised and forwarded to the right department.

Automation
Processes
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Retrieval pipeline

A retrieval pipeline is the sequence of processing steps a system uses to find the right information for a question. Typical steps are: ingesting and cleaning documents, splitting them into chunks, building a search index, optionally rewriting the query, retrieving matches, ranking them (reranking) and passing the best ones to the language model. It is the foundation of every RAG application, and its quality largely determines how good the resulting answers are.