What is Systems Engineering

26 Aug, 2025

Systems Engineering: Why Integration Matters More Than Specialisation

The “specialist versus generalist” debate is everywhere in engineering circles. Some argue that deep focus wins; others say breadth is the future. I think both sides are missing the point. The real edge today lies in integration

Systems engineers aren’t specialists or generalists; they’re integrationists

The Burger Stack Analogy

Think of a burger. You don’t just care about the bun, the beef, or the cheese in isolation. What matters is how the pieces fit together into something coherent.

Systems engineers operate the same way. They don’t just know about components at a surface level; they understand how to integrate across the stack, whether that stack is software layers, supply chains, or rocket subsystems.

What Is Systems Engineering?

At its simplest, systems engineering is the discipline of designing, managing, and sustaining complex systems across their entire lifecycle.

Key aspects include:

1. Integration across domains: software, hardware, mechanical, industrial, and more.
2. Lifecycle thinking—from initial concept to deployment to retirement.
3. Systems thinking—parts only make sense when viewed as a whole.
4. Risk awareness—anticipating failure and designing around it.

It’s a meta-skill: not engineering this one thing, but engineering the relationships between things.

Why It Matters Now

As AI continues to automate repetitive and narrow tasks, the value of being “the person who only writes backend code” or “the person who only optimises a single component” is diminishing.

The enduring skill is integration. Systems engineers thrive here because they can bridge domains and keep the system coherent when technology stacks get taller and more complex.

A Personal Lens: AI Systems Engineering

In my own work, I’d describe myself as an AI systems engineer. My focus is building AI for science, which means I can’t afford to think only about machine learning models.

I need to see the whole system:

• The software layer (FastAPI services delivering predictions).
• The machine learning layer (models, training pipelines, optimisations).
• The infrastructure layer (HPC clusters, GPUs, distributed inference).

What I’m really building isn’t just a model or an API. It’s an inference engine, a system that serves predictions reliably within defined operational constraints. That framing is what systems engineering gives me: a way to see across boundaries.

Broader Applications

The same pattern shows up everywhere:

• Aerospace engineers must harmonise propulsion, avionics, and embedded systems.
• Medical device teams must align electronics, mechanics, and software under strict safety standards.
• Large-scale software projects require not just code, but also lifecycle management, risk analysis, and integration across layers.

In all cases, systems engineers are the glue that keeps complexity from collapsing into chaos.

Closing Thought

Specialisation will always matter. So will breadth. But neither is enough on its own.

In an era where AI eats away at narrow tasks, the enduring skill is integration. Systems engineers aren’t just part of the system; they design coherence itself.

That’s the role I’ve chosen to embrace: AI systems engineering for science. It’s a field still being defined, but one that sits squarely in the future of engineering.