Quick take
Stop hiring ML researchers to do integration work. AI engineering is the craft of turning probabilistic models into reliable product features. Different job, different skills, different mindset.
After a year of working on AI integration across different organizations, the pattern I keep seeing is the same: a team hires a machine learning engineer, points them at a product feature, and wonders why the result is a brilliant notebook that falls apart the moment a real user touches it.
The problem isn’t the engineer. The problem is a category error.
This isn’t ML. This isn’t backend. It’s its own thing.
AI engineering sits in an awkward gap. On one side, you have model training – the research-heavy work of building and improving models. On the other, traditional software engineering – APIs, databases, deployment pipelines, the stuff we’ve been doing for decades.
AI engineering is neither. It’s the work of taking someone else’s model and making it do something useful, reliably, in production. That means prompt design, retrieval pipelines, evaluation harnesses, cost management, safety guardrails, and graceful failure handling. It means caring deeply about the 2% of cases where the model confidently produces garbage.
I spent years building backend systems across fintech and cloud infrastructure. The shift to AI engineering felt familiar in some ways – you still think about latency, error handling, observability. But the non-determinism changes everything. You can’t unit test your way to confidence when the same input produces different outputs on Tuesday.
The skill set looks different
When I talk to CTOs about what to look for in AI engineering hires, I push them away from the classic ML job description. The competencies that actually matter are:
- Prompt design and testing. Not prompt “engineering” as a parlor trick. Systematic testing across edge cases, with version control and regression detection.
- Retrieval and context assembly. Getting the right information to the model at the right time. This is where most applications succeed or fail.
- Integration discipline. Error handling, latency budgets, fallback paths. The boring stuff that separates demos from products.
- Evaluation loops. If you can’t measure whether your AI feature got better or worse after a change, you aren’t doing engineering. You’re doing improv.
- Safety and guardrails. Especially when the model can take actions or access private data.
None of this requires a PhD. It requires someone who has shipped software, understands production systems, and has the patience to wrangle probabilistic outputs into predictable behavior.
It’s a set of responsibilities, not a stack
People keep trying to draw AI engineering as a neat layer diagram. In practice, it’s a set of cross-cutting responsibilities. You’re choosing models, preparing data, shaping prompts, monitoring quality, controlling costs, and enforcing safety – all at once. The reason the role feels distinct is that it spans product thinking, system design, and ongoing operational care in a way that neither pure ML nor pure backend roles typically do.
At one large telecom, I watched teams try to split these responsibilities across existing roles. The ML team owned prompts. The backend team owned integration. The product team owned evaluation. Nobody owned the whole thing. The result was predictable: finger-pointing when quality dropped and no single person who could trace a bad output from user input to model response to product impact.
How to actually build these skills
Depth beats breadth. Don’t chase every new framework or technique. A solid path:
- Build a feature that calls a model and returns something useful. Ship it.
- Add retrieval so the model’s answers are grounded in real data instead of vibes.
- Build an evaluation loop that catches regressions before your users do.
- Add guardrails and define what happens when the model fails. Because it will.
The practice is learned by shipping and iterating. Blog posts help (including this one, I hope), but they aren’t a substitute for watching your carefully crafted prompt fall apart on production traffic.
Where this fits in your org
In smaller teams, AI engineering looks like a product-focused engineer who owns the AI feature end to end. At larger companies, it becomes a dedicated role that sits between product, platform, and security.
The interaction model is clean. Product defines intent and user experience. Platform provides infrastructure and monitoring. Security sets the safety bar. AI engineering turns those constraints into working features that don’t embarrass anyone.
The demand for this role is growing fast. Job descriptions are finally separating AI engineering from ML research, and the expectations center on integration, evaluation, and reliability rather than paper-publishing and model architecture. Good. That separation was overdue.
The discipline, not the hype
AI engineering isn’t a buzzword rotation. It’s the recognition that making models useful in production is real engineering work – with its own tools, its own failure modes, and its own career path. The teams that treat it as a distinct discipline are shipping better features. The teams that don’t are still arguing about whether their demo “works.”
Discipline over heroics. That’s the whole game.