The Evolving Landscape of LLM Engineering: Insights from Maxime Labonne

Large Language Models (LLMs) have rapidly evolved, and with them, the role of an LLM engineer has become more complex and specialized. In a recent conversation with Maxime Labonne, Senior Staff ML Scientist at Liquid AI and author of the LLM Engineer’s Handbook, we explored the essential skills, emerging architectures, and best practices for working with LLMs in 2025.

You can listen to the full podcast with Maxime Labonne on Spotify, Apple, and SoundCloud.

The Role of an LLM Engineer

At its core, LLM engineering is an extension of software engineering, infused with a deep understanding of LLM-specific tasks such as fine-tuning, inference optimization, and model deployment. According to Maxime Labonne, successful LLM engineers must possess strong software development skills alongside domain expertise in inference, quantization, deployment (cloud and edge), and retrieval-augmented generation (RAG) pipelines.

The emergence of LLMOps — a structured approach to managing and deploying LLMs — has further shaped the discipline. Engineers are now expected to build scalable, reusable workflows that enhance both performance and efficiency in AI-driven applications.

The Concept of an LLM Twin

One of the most compelling ideas discussed was the notion of an “LLM Twin,” a concept Maxime Labonne elaborates on in his book, The LLM Engineer’s Handbook. This involves training an LLM on an individual’s or organization’s proprietary data, allowing it to emulate the knowledge, writing style, and decision-making processes of its source.

An LLM Twin is built through a combination of fine-tuning and retrieval-based approaches. Organizations can leverage this to create AI personas capable of automating decision-making, assisting with customer support, or enhancing internal documentation processes. Labonne emphasized that proper data curation is key to achieving high fidelity in an LLM Twin.

The Rise of the FTI Architecture

Maxime Labonne introduced the FTI (Feature, Training, Inference) architecture as a systematic way to build scalable machine learning systems. Unlike ad-hoc model training, FTI ensures that pipelines are reusable across multiple models. This framework is crucial for enterprises aiming to scale LLM usage without incurring excessive overhead.

The Future of Model Architectures: Beyond Transformers

While transformers have dominated the LLM landscape, Maxime Labonne believes we are on the cusp of a post-transformer era. Alternative architectures, such as State Space Models (SSMs) and mixture-of-experts (MoE) models, are gaining traction due to their efficiency in handling long-context tasks and reducing memory footprints. These advancements could redefine how LLMs are trained and deployed at scale.

Fine-Tuning vs. Retrieval-Augmented Generation (RAG)

Fine-tuning remains a critical tool for improving LLMs, but Maxime Labonne warns against its overuse. Instead, he suggests a strategic approach:

• Use RAG for injecting external knowledge dynamically.
• Reserve fine-tuning for tasks like aligning models to domain-specific knowledge or adapting to nuanced tone and style.
• Combine both approaches where appropriate to maximize efficiency and accuracy.

He also highlighted the importance of preference alignment — a process that improves user experience by refining how models present information. This step is increasingly critical as AI systems become more interactive.

Inference Optimization and Model Merging

Optimizing inference speed and cost is a significant challenge in LLM deployment. Maxime Labonne pointed to speculative decoding, quantization, and model merging as key techniques for improving performance. Model merging, in particular, allows for combining multiple fine-tuned models to create a more powerful composite model without retraining from scratch.

The Evolving Skill Set for LLM Engineers

Given the rapid advancements in AI, Labonne suggests that aspiring LLM engineers:

• Develop expertise across the LLM stack, from data engineering to deployment.
• Specialize in areas such as inference optimization or edge deployment.
• Stay model-agnostic to remain adaptable to new architectures and frameworks.

Conclusion on LLM Engineering

The field of LLM engineering is evolving at an unprecedented pace. Whether it’s new architectures, improved inference techniques, or smarter ways to fine-tune models, staying ahead requires both breadth and depth of expertise. As AI continues to shape the future, the role of an LLM engineer will become even more integral to how businesses leverage AI for efficiency and innovation.

For those looking to deepen their knowledge, Labonne’s LLM Engineer’s Handbook offers an invaluable roadmap, providing both foundational concepts and advanced strategies to navigate this dynamic field.