If AI engineering is the connective tissue, what exactly does it connect?
In part one of this series, I made the case that AI engineering isn't just another buzzword – it's a discipline that integrates the technical, ethical and human dimensions of building AI systems. So, what are we connecting, here?
The answer lies in one of tech's most crowded areas: the "-Ops" disciplines.
The proliferation problem
You've seen the -Ops family tree expand rapidly. DevOps begat MLOps. MLOps inspired LLMOps. Meanwhile, AIOps, DataOps, and ModelOps each staked their claims. Every new paradigm brings its own tools, workflows, and – let's be honest – its own conference tracks.
This isn't inherently bad. Specialization exists because the problems are genuinely different. Deploying an LLM isn't the same as deploying a fraud detection classifier. Managing data pipelines requires a different approach than managing IT infrastructure alerts. These disciplines emerged because we needed focused frameworks for distinct challenges.
But, specialization creates silos. Teams optimize for their slice while connections between slices fray. The data engineering team throws datasets over the wall to the ML team, who throw models to the deployment team, who field complaints from operations. Each handoff is a chance for context loss, assumptions to diverge, and accountability to blur. (This might sound familiar…)
AI engineering provides the orchestration layer
AI engineering doesn't replace these disciplines. It orchestrates them. Think of it as the architectural layer that asks, “How do all these pieces work together to deliver trustworthy, scalable AI systems?”
Consider a practical example. A health care organization wants to deploy a clinical decision support tool. The journey touches nearly every -Ops domain. DataOps ensures the patient data pipelines are reliable and governed. MLOps handles the model training, versioning, and validation workflows. If the solution involves a generative component – say, summarizing clinical notes – LLMOps practices come into play for prompt management and output monitoring. AIOps might manage the infrastructure alerts and performance monitoring once the system is live.
Without AI engineering thinking, each team solves its piece in isolation. But with it, someone asks harder questions: “How do data governance decisions upstream affect compliance downstream?” “How do we design monitoring so that model drift triggers the right response across teams?” “How do we build feedback loops that actually close?”
This isn't theoretical. Anyone who's tried to trace a production issue back through multiple teams knows how quickly "not my problem" becomes the default. AI engineering makes it everyone's problem – in the best sense.
The unifying principles of AI engineering
What does AI engineering bring to the table that individual -Ops disciplines don't?
End-to-end accountability
The -Ops disciplines tend to optimize for handoffs – getting artifacts from one stage to the next. AI engineering optimizes for outcomes, ensuring the entire system delivers value reliably. This shifts the focus from "my piece works" to "the whole thing works."
Shared governance
Data governance, model governance, and operational governance often live in separate documents maintained by separate teams. AI engineering creates the connective tissue – common frameworks for risk assessment, explainability standards and audit trails that span the lifecycle. When regulators come asking questions, you don't want three teams pointing at each other.
Adaptive resilience
AI systems don't just need to work; they need to keep working as the world changes. AI engineering embeds the expectation that models drift, data distributions shift, and requirements evolve. It builds the feedback loops and monitoring infrastructure to detect and respond, rather than treating these as edge cases.
Coalition, not conquest
Let’s be clear: AI engineering isn't here to absorb or eliminate the -Ops disciplines. MLOps practitioners aren't suddenly obsolete. DataOps expertise remains essential. The goal isn't consolidation for its own sake.
Instead, AI engineering provides a shared language and shared priorities. It's the difference between a group of specialists who happen to work on the same project and a team with a common understanding of what success looks like. The -Ops disciplines bring depth; AI engineering brings coherence.
There's a bit of a parallel here to open source collaboration – something I think about a lot. When teams contribute to shared projects rather than maintaining private forks, the cost of "eternal vigilance" gets distributed. Quality improves because more eyes catch more problems. The same logic applies to AI engineering: when disciplines collaborate under a shared framework, the burden of integration doesn't fall entirely on any one team.
This matters especially as AI systems grow more complex. When you're deploying a single model to a single endpoint, you can probably get by with informal coordination. When you're managing dozens of models, multiple data sources, generative AI components, and real-time monitoring – all under regulatory scrutiny – informal coordination breaks down. You need engineering discipline. You need AI engineering.
The human element
Here's what excites me most: it creates space for roles and perspectives that don't fit neatly into any single -Ops box. The ethicist who asks hard questions about fairness. The domain expert who knows what the numbers mean. The designer who ensures the system is usable by the people who need it.
AI engineering doesn't just unite technical disciplines – it creates an on-ramp for the human judgment that makes AI systems trustworthy. In part one, I talked about collaboration as the antidote to tunnel vision and bias. This is where that collaboration lives…not in abstract principles, but in the concrete work of building systems that hold together under pressure.
Looking ahead
The -Ops landscape isn't going to simplify. Models will grow more complex, regulations more demanding, stakes higher. The question isn't whether we need a unifying discipline. It's whether we'll build it intentionally or let it emerge haphazardly. I vote for intentional.
We've talked about integration, orchestration, and shared accountability across technical disciplines. What we haven't addressed is why that accountability matters – and to whom.
AI systems don't operate in a vacuum. Decisions affect hiring, healthcare, lending, and criminal justice. They shape what information people see and how they see it. The technical rigor we've discussed isn't just about building systems that work – it's about building systems that work for people, equitably and transparently.
In part three, we'll turn to the ethical and social imperatives of AI engineering. Not as an afterthought or a compliance checkbox, but as a core discipline requirement. Because getting the architecture right matters a lot less if we're architecting the wrong outcomes.
