AI tools are transforming how we develop apps, websites, and features, but there’s some confusion about two roles: AI engineers and AI coders. They both play crucial parts, yet the skills, stack, responsibilities and focus areas differ. So what’s the difference between the two? Let’s break it down.

AI Engineer: The Architect

Think of AI engineers as the ones who design and build the intelligent systems that run under the hood. They work on the big picture - developing algorithms, building models, and ensuring AI systems run efficiently and securely at scale. AI engineers are architects of intelligent systems, focusing on the “how” of making technology learn and predict.

What AI Engineers Do:

  • Develop AI Models & Algorithms: Engineers create solutions using machine learning (ML), natural language processing (NLP), and computer vision to power applications.
  • Deploy and Optimize Models: They ensure the models work smoothly in cloud environments like AWS or GCP.
  • Handle Data at Scale: Analyze massive datasets and ensure the AI can draw meaningful insights from them.
  • Solve Complex Problems: Engineers tackle issues like bias in algorithms and optimize models to be ethical and effective.
  • Collaborate with Data Scientists and DevOps: They often work with other experts to deploy AI solutions securely and seamlessly.

Examples of Projects:

  • Predictive analytics for fintech apps (forecasting market trends)
  • Advanced chatbots powered by ML models
  • Image recognition for medical diagnostics

AI Coder: The Developer

Now, AI coders (myself included) are more hands-on with AI APIs and frameworks. We don’t create algorithms from scratch but use existing tools to build applications fast.

What AI Coders Do:

  • Integrate AI APIs: Coders use APIs like OpenAI, Hugging Face, or Google’s Cloud Vision to add features such as text analysis, speech recognition, or chatbots to websites.
  • Build Front-End and Back-End Systems: They’re familiar with JavaScript frameworks like React or Next.js, ensuring smooth integration between web interfaces and AI.
  • Optimize User Experience (UX): AI coders focus on how users interact with AI features, such as recommendation engines, virtual assistants, or personalized dashboards.
  • Rapid Prototyping: Since coders rely on APIs, they can whip up prototypes quickly, testing how well an AI model fits a product.

Examples of Projects:

  • Integrating a chatbot for a customer service page
  • Adding sentiment analysis to user reviews on an e-commerce site
  • Building a recommendation system using an off-the-shelf AI API

Comparing the Two Roles

Where the Roles Overlap

There’s some overlap between these roles, especially as AI APIs become more powerful. For example, an AI coder might need to fine-tune a pre-trained model to suit a particular product, dipping a toe into the engineer’s world. Meanwhile, AI engineers may have to tweak front-end components to ensure their models integrate smoothly into apps.

Small teams or startups may have developers who wear both hats -coding features while dabbling in data science. Larger enterprises, though, tend to keep these roles distinct.

LearnWeekly will mainly cover AI coding, focusing on web React/Next.js/AI API projects. As we learn together, we might add more content about AI models and their technicalities.