Introduction to AI Engineering

AI Engineering is the practice of designing, building, and maintaining AI systems that are reliable, scalable, and production-ready. It's not just about training models—it's about integrating them into real-world applications with the right data pipelines, testing infrastructure, monitoring tools, and deployment workflows. It's where software engineering meets data science, DevOps, and machine learning. As generative AI technologies, such as large language models (LLMs) mature, AI Engineering becomes even more critical. Teams need robust systems to ensure these models are safe, useful, and cost-effective in production. In short AI Engineering ensures your models don't just exist – they deliver value in real-world systems.

Differentiating AI Roles:

ML Researcher / Data Scientist: Primarily concerned with model development—designing, training, and evaluating machine-learning algorithms.
ML Engineer: Builds and maintains reliable data workflows and end-to-end pipelines for training and serving models in production.
Full-Stack Engineer: Develops the user-facing products and underlying platforms, integrating front-end, back-end, and database components.
AI Engineer: Leverages large language models to architect and implement complex chains and agents, crafting the tooling and infrastructure that enable LLM-driven applications.

The visual breakdown of the roles can be seen here:

Figure 1: Differentiation AI Engineering[2]

The components of AI Engineering in our understanding are the following:

Data Component: Build Trustworthy Data Pipelines
The LLM component: Unleash your foundation model
Retrieval vs finetuning? Choosing the Right Knowledge Strategy
Agents and Agentic Networks
Ship & Serve at Scale: The Infrastructure Layer
Watch, Measure, Improve: Monitoring & Observability

In this post, we go through each of the components

Build Trustworthy Data Pipelines

An AI system is only as good as the freshness and trustworthiness of the data it sees. Large-language models ship “empty-headed” — they know nothing about your private documents or customer records. Retrieval-Augmented Generation (RAG) bridges that gap by piping vetted data into each prompt at run time. A robust RAG pipeline boils down to five moving parts:

Source integration —connect file shares, SaaS apps, and databases.
Pre-processing —clean, redact, and normalise raw content.
Chunking —split documents into search-friendly bites without breaking context.
Embedding generation —map each chunk to a vector that captures meaning.
Vector storage —persist embeddings in a specialised index for low-latency retrieval.

First, let's look at the models themselves.

Unleash Your Foundation Model

Foundation models are massive pre-trained models that can be adapted to a wide variety of tasks with little additional training. They serve as the backbone of most modern AI applications today. Examples include:

GPT-4, Deepseek, Claude, LLaMA for text
Whisper for speech
SAM, CLIP for vision

These models are general-purpose and unlock new capabilities like:

Language understanding and generation
Code generation
Multimodal reasoning

Typically, in AI Engineering, you would use the LLM through an API and build upon it. However, there are some problems using the foundation model directly as is:

A plain LLM only “knows” what it saw during pre-training (and fine-tuning). If you need to answer questions about your company's docs, product specs or any dynamic dataset, it can't do that out of the box
LLMs can “hallucinate”—they'll confidently make up facts

To solve these problems, there are two ways

Retrieval or Fine-Tune? Choosing the Right Knowledge Strategy

Fine-tuning adapts a pre-trained LLM to a specific task by training it on domain-specific data. For example, a pre-trained LLM can be fine-tuned on financial documents to improve its financial knowledge. However, fine-tuning has several downsides compared to retrieval-augmentation:

Forgetting —fine-tuning can overwrite pre-training; a fine-tuned model may flub small-talk.
Data-hungry —quality results depend on large, costly labelled datasets.
No live context —knowledge stops at the training cut-off; real-world updates are invisible.
Hard to iterate —any change means another expensive re-training cycle.

In contrast, RAG systems:

Retain capabilities from pre-training since the LLM itself is not modified.
Augment the LLM with customizable external knowledge sources like databases.
Allow changing knowledge sources without retraining the LLM.
Have lower data requirements since the LLM is not retrained.

Therefore, RAG systems often achieve better performance than fine-tuning while retaining more capabilities of the original LLM. If we want to compose a retrieval, we could define a simple AI workflow (e.g., retrieve data → run model → return result). Once a simple workflow augments the LLM with fresh knowledge, the next leap is giving the system autonomy—enter agents. One thing to keep in mind is: n client projects, we typically start with a RAG baseline before scaling towards agents.

From Static Pipelines to Autonomous Agents

AI agents are a step beyond simple AI workflows. An agent is an autonomous system that can perceive its environment, make decisions, and act—often iteratively—toward a goal. In the context of LLMs, agents:

Maintain state and memory
Use tools or plugins (e.g., calculators, search engines)
Make decisions based on intermediate results
Plan, reason, and execute multi-step tasks

Unlike traditional AI workflows, which are often static sequences of operations (e.g., retrieve data → run model → return result), agents operate in a dynamic feedback loop. They decide what to do next based on their current state, previous actions, and goals, much like a human assistant

Figure 2: Overview of an AI Agent[1]

Ship & Serve at Scale: The Infrastructure Layer

To support production-grade AI systems, one needs to build scalable infrastructure that can efficiently handle both the training of custom models and the inference workloads of foundation models. Key principles include:

Elastic compute
Separation of concerns
Batch vs. real-time serving
Model versioning and deployment automation
Monitoring and autoscaling

Detailing the infrastructure stack would take an article of its own, so here we'll stay focused on what happens after the model is live: monitoring, guard-rails, and continuous improvement.

Watch, Measure, Improve: Monitoring & Observability

As discussed in the previous section, agents decide what to do at runtime: they pick tools, sequence calls, fuse results, and sometimes even update their own goals. With this amount of autonomy, there is also a lot of potential for something to go wrong. This is the reason to have a tight feedback loop for agents.
Without a good monitoring system, we are open to risks like:

Hidden failure paths: The agent calls a tool with the wrong parameter and doesn't get an answer. It then hallucinates a fallback answer
Latency: One slow tool call pushes the execution time past the agreed SLA
Unbounded cost: A user-supplied query forces 4 chain-of-thought calls and 50000 tokens.
Safety & compliance: A new slang term slips past your banned-word list; the agent repeats it verbatim.

To summarize: An agent is effectively a tiny orchestrator running unvetted code written in natural language. If you can't see each decision it makes—and measure the latency, cost, and quality of that decision—you can't guarantee reliability, trust, or margin. We will tackle the mentioned challenges by implementing a telemetry layer into our agentic workflow. The precise wiring of this is a story for a future post. Stay tuned for our next post, where we implement a simple agent with robust monitoring.

Machine Learning Architects Basel

Curious how this works in practice? MLAB helps Swiss organisations build resilient, auditable AI systems – step by step. Let's talk, contact us without any hesitation.

Stay tuned for our upcoming webinars and series of blog posts diving deeper into each of the lifecycle stages introduced above.