Artificial intelligence is no longer the stuff of science fiction or confined to academic labs. It’s a real, fast-evolving force transforming everything from search engines to healthcare. At the heart of this revolution is a new kind of discipline: AI engineering.
While the term may sound like a rebranding of machine learning (ML) engineering, AI engineering represents a distinct and rapidly growing field. It’s not just about training models—it’s about integrating powerful pre-trained systems into real-world applications, building the infrastructure to support them, and continuously evaluating and optimizing their performance.
Let’s explore what AI engineering is, how it differs from traditional ML engineering, and the evolving stack that defines this exciting discipline.
What Is AI Engineering?
AI engineering refers to the practice of building, adapting, and deploying artificial intelligence systems, particularly those using foundation models like GPT-4, Claude, or Llama 3. Unlike traditional machine learning, which often involves building models from scratch, AI engineering focuses on leveraging pre-trained models and integrating them into user-facing applications.
In today’s landscape, AI engineers are in high demand—particularly in large tech companies and startups that build or use language models and multimodal systems. Their role spans technical domains: they may start with writing code and prompts, continue with optimizing model inference, and end with deploying a full-scale AI-powered product.
Most AI engineers start out as software engineers. What sets them apart is not necessarily deep ML research knowledge but a deep understanding of how to apply large models in practical, performant, and safe ways.
The AI Engineering Stack: Three Foundational Layers
To understand what AI engineers actually do, it’s useful to break down their work into three core layers: application, model development, and infrastructure.
Application Layer: Building Interfaces and Experiences
This is the topmost layer, where AI meets the user. AI engineers at this level build products that utilize foundation models through APIs or embedded systems.
They write and refine prompts, design user interfaces that make AI feel seamless and helpful, and evaluate model outputs for quality, relevance, and safety. They also ensure smooth integration into larger applications using tools like LangChain, LlamaIndex, or custom APIs.
Because model outputs are often open-ended and unpredictable, evaluating them requires careful human testing or new automated metrics. Aligning AI responses with user expectations and product goals is also a key challenge.
Model Development: Tuning Intelligence
While many AI applications use pre-trained models as-is, some require adaptation to specific tasks, languages, or domains. That’s where the model development layer comes in.
This includes fine-tuning or retraining models, implementing reinforcement learning from human feedback (RLHF), curating and labeling datasets, and evaluating different model versions.
Two key adaptation techniques dominate this space. Prompt engineering offers a fast, cost-effective way to guide a model’s behavior by modifying inputs, while fine-tuning changes the model’s internal parameters for deeper customization. Each has its tradeoffs, with prompt engineering being easier to experiment with and fine-tuning offering stronger performance improvements—at the cost of more resources and expertise.
Frameworks like PyTorch, TensorFlow, Hugging Face, and experiment tracking tools such as Weights & Biases are commonly used in this layer.
Infrastructure: The Backbone of AI Systems
Beneath all the intelligence is the scaffolding: infrastructure. This layer supports both the application and model development layers with compute, data management, orchestration, and monitoring.
AI engineers here manage the infrastructure for training and inference, optimize serving pipelines for latency and cost, monitor systems for unexpected behavior, and handle massive datasets and model artifacts.
Whether it’s using Kubernetes to orchestrate containers, Ray to scale workloads, or leveraging cloud platforms like AWS and GCP, this layer is critical to ensuring AI applications remain reliable and cost-efficient—especially as models grow in size and complexity.
How AI Engineering Differs from ML Engineering
While closely related, AI engineering and ML engineering differ in both mindset and methodology.
ML engineers traditionally focus on developing models from scratch using structured data and well-defined metrics like accuracy or precision. Their work often centers around building pipelines that go from raw data to a trained model evaluated on a clear-cut task.
AI engineers, in contrast, start with foundation models and adapt them for specific use cases. They’re less focused on inventing new algorithms and more concerned with prompt design, fine-tuning, deployment, and evaluation in messy, real-world contexts. Their challenges are often less about mathematical precision and more about human alignment, safety, and ambiguity.
In practice, AI engineers blend the skill set of a backend engineer, a data scientist, and a product designer—comfortable with APIs, models, and user experience all at once.
The Rise of Foundation Models
The explosion in foundation models like GPT-4, Codex, Claude, Gemini, and Llama 3 has transformed the AI landscape.
These models are trained on massive, diverse datasets and capable of performing a wide range of tasks with little or no additional training. Their availability—either via API or open weights—has allowed teams to build AI-powered products without needing to start from scratch.
This has shifted the engineering focus. Instead of asking, “How do we build a model that does this?” teams are now asking, “How do we make the best use of the models that already exist?”
The bottleneck has moved from model development to application design, adaptation, and deployment—precisely the domains AI engineers are mastering.
The Evaluation Challenge
One of the thorniest problems in AI engineering is evaluation. Traditional ML models are evaluated using clear metrics. But how do you evaluate a chatbot’s answer? Or a generated paragraph of code? Or a personalized email draft?
Because outputs from LLMs are subjective, open-ended, and context-dependent, traditional metrics fall short.
Strategies for evaluation are still evolving and include human preference testing, rubric-based scoring, task-specific success rates, and even automated model judges. However, each approach comes with tradeoffs in cost, scalability, and reliability.
In many ways, evaluation is becoming one of the most creative and important parts of the AI engineering process.
Optimizing Inference: Speed, Scale, and Cost
Large models are not cheap to run. Whether you’re paying per token for API usage or managing your own infrastructure, inference can quickly become a bottleneck in terms of performance and cost.
This is why inference optimization is a core part of AI engineering today. Techniques include:
- Model quantization to reduce memory and improve speed
- Request batching and caching to reduce redundant computations
- Using smaller models for less demanding tasks
- Fine-tuning models to reduce prompt complexity
- Deploying models closer to end users to minimize latency
The goal is always to balance performance with cost-effectiveness—making sure users get fast, high-quality results without blowing the budget.
A Multidisciplinary Role
AI engineers rarely work in isolation. Their role often spans teams and departments.
They work with product managers to define use cases, with UX designers to shape how users interact with AI, with DevOps to manage deployment and monitoring, and with legal or compliance teams to address privacy and fairness concerns.
Success in AI engineering requires not just technical expertise, but collaboration, adaptability, and a deep curiosity about how humans interact with intelligent systems.
Looking Ahead
AI engineering is reshaping how we build software.
Rather than coding every behavior from scratch, engineers now design systems that collaborate with models—systems that can reason, summarize, translate, generate, and learn. This is a profound shift, and we’re only at the beginning.
As foundation models grow more powerful, the demand for skilled AI engineers will only increase. But raw technical power isn’t enough. What matters most is thoughtful integration, continuous evaluation, and a relentless focus on delivering real value to users.
If your organization is exploring how to bring AI into your product or workflow, we’d love to help. Zarego specializes in building and deploying AI-powered systems that are secure, scalable, and user-friendly.
Contact us to start the conversation.
