Beyond Prompt Engineering: What an AI Engineer Actually Does
The title "AI Engineer" is becoming more common, but what does the job actually entail? While some might imagine engineers fundamentally altering LLM architectures, the reality for most is more grounded in practical application development. The role is less about creating new models from scratch and more about skillfully building systems with them.
The Core of the Work: Building with AI
For many who identify as AI Engineers—or software engineers and data scientists tasked with AI implementation—the daily work revolves around using pre-existing models and services. This involves a distinct set of technical tasks:
- System Integration: A primary function is writing the "glue code" that connects vendor LLM APIs (like OpenAI's or AWS Bedrock) to other applications and data sources.
- RAG Implementation: Building Retrieval-Augmented Generation (RAG) systems is a common project. This involves using vector databases to provide the LLM with relevant, up-to-date context to answer queries accurately.
- Evaluation and Safety: The job isn't just about getting a response. It includes running accuracy metrics against ground truth data, implementing guardrails to filter harmful content, and using tools like rerankers to improve the quality of search results.
- Classic Software Engineering: Foundational software practices are critical. This means implementing resilience patterns like rate-limiters, debugging complex issues across distributed systems, and taking prototypes all the way to production.
More Than a Coder: The Strategic Element
A crucial, non-technical aspect of the AI Engineer role is managing expectations and defining project scope. A significant portion of the job is spent in dialogue with stakeholders, repeatedly asking, "What do you want?" to clarify requirements.
Equally important is defining the failure path: "What should happen when the AI cannot answer?" This foresight prevents poor user experiences and system failures. Furthermore, a skilled AI Engineer acts as a strategic advisor. This includes staying current on the rapidly evolving technology landscape and, critically, knowing when to tell a client or team that Generative AI is not the right tool for their problem, suggesting simpler, more reliable solutions when appropriate.
A Role in Flux
The title "AI Engineer" means different things in different organizations. An engineer at a foundational model company like OpenAI will have a vastly different role than one at a company applying AI to its products. Many professionals doing this work still hold traditional titles like "Senior Software Engineer" or "Data Scientist," highlighting that the role is often an evolution of existing skill sets applied to a new and powerful toolkit.