I’ve been in IT for over two decades – Oracle databases, retail systems, insurance platforms. A few years ago, I started hearing “AI” everywhere. But when I looked closer, most people were just prompting ChatGPT or running a notebook once. That’s not enough for a business that needs reliable, repeatable results.

AI engineering is the discipline of taking artificial intelligence out of the lab and making it work inside your actual operations. It’s the bridge between a cool demo and a system that processes your invoices, forecasts stock, or answers customer calls at 2 AM – without breaking.

🔧 What I actually do as an AI engineer

In my work with insurance companies and retailers across Kenya and Tanzania, I don’t just train models. I integrate them. That means:

• System integration: Hooking up LLMs via APIs, using vector databases to remember your product catalogue, and RAG (Retrieval-Augmented Generation) so the AI answers based on your real data – not some internet hallucination.
• Production focus: I make sure the AI is fast, cheap enough, and scales when your business grows. A model that works on a laptop but crashes on your server is useless.
• End‑to‑end workflow: Data preparation, fine‑tuning models, and managing the infrastructure (cloud or on‑prem). I’ve done this for Oracle EBS, Power BI, and now generative AI tools.

Real‑world examples? I built an investment portfolio system using Oracle APEX and added an AI layer for risk prediction. I’ve designed data warehouses for Power BI dashboards that now use AI to flag anomalies in sales – automatically.

🧠 The skills that matter (from someone who uses them daily)

You don’t need a PhD. You need practical chops:

• Programming: Python is my go‑to, but Java and even PL/SQL matter when you work with legacy systems.
• AI frameworks: I use TensorFlow and PyTorch for custom models, but more often I’m fine‑tuning smaller LLMs or using APIs from Claude, Gemini, and OpenAI.
• Infrastructure & MLOps: Docker, Kubernetes, cloud platforms (AWS SageMaker, Azure ML, Google Vertex). I also rely on Oracle Cloud and Azure AI because my clients are already there.

And honestly? The most underrated skill is understanding business logic. I can’t help a retailer if I don’t know how their inventory moves.

📊 AI engineering vs. data science – the simple difference

A data scientist builds and trains a model to find insights. An AI engineer takes that model (or a pre‑trained one) and turns it into a reliable service that your team actually uses. Think of it like this: data science gives you a recipe; AI engineering builds the kitchen and serves the meal every day, non‑stop.

At Reliance Insurance and IAPPS Africa, I’ve lived both sides. I’ve audited IT infrastructure, upgraded Oracle databases, and then deployed AI that watches those systems for failures. That’s the hybrid advantage.

🚀 Why this matters for your business

You don’t need a massive AI lab. You need someone who can connect the dots between your existing tech (Oracle, Power BI, Linux, whatever) and the new wave of AI. That’s exactly what I do – with a focus on East African realities: unpredictable connectivity, budget constraints, and real people using the tools.

Bottom line: AI engineering is about getting work done with less effort. That’s my motto, and it’s the only way I know how to consult.

– Manish Chudasama, AI Consultant & Oracle Specialist