AI Engineers, ML Engineers, And LLM Engineers: What Do These Roles Actually Do?
Companies everywhere are racing to hire “AI talent.” The problem is that not every AI role is the same. Titles like ML Engineer, AI Engineer, and LLM Engineer often get mixed up, leading to the wrong people hired for the wrong jobs.
Understanding the differences is more than a technical detail. It can be the difference between an expensive experiment that goes nowhere and an AI project that delivers real business value.
So what do these roles actually involve, and how should hiring managers think about them?
AI Engineers: Adapting AI to Business Needs
AI Engineers are generalists who connect the dots between business needs and technical solutions. While ML Engineers focus on building infrastructure, AI Engineers work more broadly across different areas to make sure AI solves real problems.
Core responsibilities include:
Integrating AI solutions: Adding AI models or services into existing products and systems.
Problem framing: Working with business teams to understand their needs and turning those into practical AI problems.
Tool flexibility: Choosing from a range of tools — like machine learning, natural language processing, or computer vision — to deliver the right solution.
AI Engineers are less about inventing new technology and more about applying what already works. Their value lies in knowing how to use AI tools effectively to solve business challenges.
Machine Learning (ML) Engineers: Building and Running the AI Systems
ML Engineers focus on making models usable at scale. Their remit is less about creating new algorithms from scratch and more about ensuring the pipeline from data to deployment is robust.
Core responsibilities include:
Model Deployment: Taking models from research notebooks and embedding them in production environments.
Infrastructure & Scaling: Designing data pipelines, APIs, and workflows so models run efficiently under real-world loads.
Monitoring & Maintenance: Tracking drift, retraining models, and ensuring long-term reliability.
An ML Engineer is often the bridge between data scientists and software engineers – less about experimentation, more about engineering discipline. They make AI real for the business.
Large Language Model (LLM) Engineers: Specialists in Large Language Models
LLM Engineers focus on one of the fastest-growing areas of AI: large language models like GPT or Llama. Their role is different from ML or AI Engineers because they work specifically on adapting these models for business use.
Core responsibilities include:
Prompt design and testing: Writing and refining instructions so models give useful, reliable answers.
Connecting to company data: Using techniques like retrieval-augmented generation (RAG) so models can provide responses based on internal information.
Customisation and fine-tuning: Adjusting models to fit a company’s industry, products, or customers.
Safety and compliance: Making sure outputs are accurate, secure, and meet business or regulatory requirements.
LLM Engineers are highly specialised. They help organisations get real value from language models while managing risks such as cost, accuracy, and trust.
Why the Distinctions Matter
Treating these roles as the same is a common mistake. Each one brings different strengths. An ML Engineer may be great at building reliable systems but not skilled in designing prompts for language models. An LLM Engineer may be strong with generative AI but less focused on infrastructure.
If hiring managers do not understand the differences, the wrong people get hired for the wrong jobs. That often leads to wasted budgets, stalled projects, and frustration for both the company and the employee. The key is matching the role to the business need.
Conclusion: Clear Roles, Better Outcomes
The AI talent market is crowded with new titles and rising salaries. But successful companies cut through the noise. They focus first on the business problem, then match it with the right role — ML Engineer, AI Engineer, or LLM Engineer.
Hiring the right person is not just about filling a seat. It is about making sure the skills align with the task, whether that is scaling systems, integrating AI into products, or adapting large language models. Clear definitions lead to better hires, stronger teams, and AI projects that actually deliver value.