What Is an AI Engineer? Roles, Skills, and the Future of LLM‑Powered Systems

Introduction

Recent discussions about AI engineering have resurfaced, surprising the author who expected the role to have settled after the LLM boom. The article aims to provide a one‑stop guide for anyone wanting to enter or advance in the AI engineer role.

Outline of the Article

Evolution of AI systems in the LLM era

Differences between AI engineering, machine learning, and software engineering

Required skills for AI engineers

Typical daily work

Future outlook

1. Evolution of AI Systems in the LLM Era

AI systems have not fundamentally changed; the introduction of LLMs adds new capabilities to existing pipelines. The key new abilities are:

Planning

Content extraction

Content generation

Code generation

These capabilities enable more powerful, autonomous applications when combined with traditional software and ML components.

Key Roles

AI Researchers focus on pre‑training or post‑training LLMs, creating models that later become part of AI systems. Recent attention has shifted to post‑training, producing products like OpenAI o1, which, while still limited, improve reasoning capabilities.

AI Engineers use pretrained LLMs to build production AI systems that solve real business problems. Their work has progressed from simple prompt‑answer setups to Retrieval‑Augmented Generation (RAG), Agentic RAG, and increasingly complex multi‑agent systems. In the next two years, reliable deployment of multi‑agent systems is expected, possibly leading to semi‑autonomous agents by 2026‑2027.

“The goal of an AI engineer is to leverage existing resources to build an AI system that solves real business problems.”

2. Comparing AI Engineering with ML and Software Engineering

Building an LLM‑based product can be quick—connect to an API, design prompts, and integrate with a UI. However, once the system starts failing, the lack of observability, evaluation, and deterministic behavior becomes apparent. Software engineers often lack experience with nondeterministic systems, which is the domain of ML engineers.

Historically, the AI engineer role was viewed as unnecessary, but practical experience shows that without solid engineering foundations, projects quickly collapse.

Typical Skill Gaps

AI Researchers : strong in prototyping and statistical foundations but may lack production deployment experience.

ML Engineers : skilled in building and deploying ML models, data pipelines, and feature stores, but may lack deep research or large‑scale system design.

Software Engineers : excel at building high‑throughput, low‑latency deterministic systems and DevOps, but often lack experience with nondeterministic AI components and continuous learning.

Most AI engineers combine expertise from two of these three domains, often falling into one of three archetypes illustrated in the article.

3. Skills Needed to Succeed as an AI Engineer

The field evolves rapidly; staying current with research (e.g., prompt formatting studies) is essential.

Research : Read papers, whitepapers, and industry blogs to evaluate what works for your organization.

Prompt Engineering : Master prompt design, multi‑prompt dependencies, shared state, and develop custom evaluation datasets.

Software Development : Apply solid software engineering and DevOps practices to ensure robustness.

Infrastructure : Deploy workloads, understand new storage systems like vector databases, and integrate data pipelines.

Data Engineering : Spend significant time cleaning, understanding, and integrating data sources into AI applications.

MLOps (AgentOps) : Adopt proven MLOps practices for AI systems, including evaluation, observability, prompt versioning, and feedback loops.

“Not everything is about prompts; the hardest part is often integrating internal data sources into your AI application.”

4. What a Day in the Life of an AI Engineer Looks Like

Much of the work is non‑engineering: guiding the organization on whether LLMs truly solve a business problem, staying up‑to‑date with research, and building test datasets for evaluation. Collaboration with front‑end teams for feedback loops is crucial. Engineering work begins only after the research and evaluation phases are solid.

5. The Future of AI Engineering

In the coming years, every company will have autonomous workflows powered by AI, making AI engineers indispensable. Salaries will remain high due to talent scarcity. 2025 is expected to be the “agent year,” with 2026‑2027 seeing widespread multi‑agent and autonomous systems. Full‑stack AI engineers will be especially valuable for building new startups with minimal resources.