Engineers, Everywhere

Scan a tech careers board today and you’ll spot a pattern: Design Engineers, GTM Engineers, Product Engineers. Titles that didn’t exist five years ago. They’ve appeared for one simple reason: AI has collapsed the distance between idea and technical execution. When anyone can prototype with a prompt, the people who understand both the business problem and the engineering possibilities become the highest-leverage hires.

One of those new titles—AI Automation Engineer—gets half the story right. Yes, these builders wield LLMs, Zapier, Retool, and APIs to eliminate repetitive tasks. But reducing the role to “automation” understates its scope. What actually needs engineering is the entire operational system: the messy lattice of tools, processes, and knowledge that lets a business run day to day.

That fuller mandate is why we use a broader label: Ops Engineer.

The Essence of the Role

Ops Engineers develop the internal tools that power a company’s operations—combining data, workflows and interfaces into one cohesive, resilient operational system.

Ops Engineers bridge operations and engineering. They think of a company’s operations as a product and of its employees as users.

They map every data source, every Slack-to-CRM copy-paste, every manual bottleneck, pick the right tools, build custom ones and automate. Sometimes that means a perfectly-timed Slackbot; other times a lightweight internal app that finishes a task in ten seconds instead of five minutes.

Whatever they build, the goal is always the same: systemic efficiency at scale.

How Ops Engineers Work

1. Discovery

The first mission of Ops Engineers is to gain clarity. They shadow account managers, go through Google Sheets, interview the finance team about that monthly “CSV-into-Excel-into-ERP” ritual. They draft a living map of how information moves (or fails to) and highlight where drops, duplications, and delays cost time and money.

2. System design

Ops Engineers are the architects of the operational system. They gather requirements and constraints, survey and benchmark potential tools, and make opinionated recommendations that deliver both short-term value and a scalable foundation.

3. Data structuring

Ops Engineers make data trustworthy before they automate. They centralize data from all the available sources, make the schemas clear, dedupe records, and wire in observability.

4. Interface development

They set up the right tools and build custom ones when needed, shipping anything from Airtable bases to fully fledged internal apps, partnering with the engineering team when it should live in code.

5. Workflow setup

‍Ops Engineers know that a five-step Zap can be perfect, until an unforeseen edge case blows it up. They decide case-by-case:

• Should this task vanish entirely? Let’s build a no-code automation.
• Is this workflow high-volume or high-complexity? Let’s turn it into code to save costs.
• Should we use AI? Only if it’s the best lever: on clean data, with guardrails and a fallback.
• Do we need a human in the loop? A tiny Retool frontend or Slack bot can compress the task from minutes to seconds without switching context.

6. Measurement

Every release carries a unit of value: hours saved, tickets deflected, cash collected sooner, errors avoided. Ops Engineers instrument those numbers because they know efficiency only matters if you can prove it repeatedly.

7. Docs & training

Because tools are useless if nobody feels confident using them, Ops Engineers host lunch-and-learns, write “How this works” docs, record Loom walkthroughs, and build guardrails that make adoption intuitive. The handover ethos: sustainable autonomy, not heroic maintenance.

What Great Ops Engineers Are Made Of

Ops engineers are first and foremost… engineers. But they also need to sketch the system, pick the right tool, write the glue, and bring people with them.

• Full-stack engineering. Comfortable moving from a quick script to a small service to a lightweight UI, shipping end to end without hand-offs.
• Product sense & basic UX/UI. Enough taste to design small, clear interfaces that reduce a five-minute task to ten seconds.
• Strong data & backend chops. SQL and schemas, APIs and webhooks, job queues and reliability patterns: the plumbing that makes workflows dependable.
• Tooling fluency. Knows the landscape (CRMs, billing, support, ETL, low-code) and benchmarks pragmatically: build when it matters, buy when it compounds.
• AI as leverage. Uses LLMs for enrichment, summarization, classification, agents, and copilots, always on top of trustworthy data and with guardrails.
• Communication that drives adoption. Clear docs, crisp demos, and operator empathy able to explain the “why,” not just the “how,” so teams actually use what’s built.
• Alignment across functions. Can sit with finance at 10, support at 11, and engineering at 2 reconciling incentives and shipping systems that work for everyone.

Where Do These People Come From?

• Operators who started hacking. Think of the RevOps lead who was tired of having to find workarounds in HubSpot and quietly taught herself SQL and TypeScript so she could stitch the CRM to a data warehouse and a billing API.
• Engineers who love turning chaos into harmony. The internal-tools dev who’d rather fix ten back-office headaches & save its team hundreds of hours than iterate on a UI microinteraction. They see inefficiency as a puzzle worth solving.

Both profiles share a habit: they think in systems.

Real Outcomes for Real People

Remote trimmed over a quarter of IT tickets by letting AI resolve them automatically (about $500 k and 2,000+ staff days saved each year).

Vendasta reclaimed $1M in annual pipeline by automating sales follow-ups and lightening admin.

A Railblocks client has an operational system that lets a single person handle a workload that used to demand 5.

In every case, the lever wasn’t a tool in isolation. It was a person who could see the entire system and design an architecture to streamline it.

So… Is This Just RevOps 2.0?

RevOps, CRM admins, automation specialists: they’re all vital. They deepen expertise in single platforms or processes.

Ops Engineers go wider. They write code, orchestrate LLMs, choose or build tools, and care about dependencies across departments. They’re not there to replace operators; they’re there to amplify them by engineering away the drudge work.

And while the “AI Automation Engineer” role overlaps heavily, automation is only one of three levers (data, automation, interfaces) and AI is only one of the technical layers.

Framing the job as Ops Engineering signals the full responsibility and the strategic weight of running the company’s internal engine.

Who Needs an Ops Engineer?

• Start-ups who want to optimize revenue per employee and don’t have to tie revenue growth to headcount growth.
• Tech-enabled service firms where human time is the primary cost.
• SaaS companies that need proprietary, compliant back-office code (health, finance, cybersecurity).
• Founders who want to own their internal system, turning it into a differentiating asset that gets valued at exit.
• Any team staring at a forest of spreadsheets and thinking “There has to be a better way”.

Ops Engineers don’t promise magic. They promise an operating system that grows with you and lets every teammate do their highest-value work.

The Bigger Picture

Operations has always been the shadow product of a company: too critical to fail, too ad-hoc to own. AI and low-code didn’t merely make automation easier; they made it inevitable that someone would step up to engineer the whole environment.

That someone is the Ops Engineer.

Empower them and your org becomes the kind of well-oiled machine customers feel but rarely see: accurate data, smooth workflows and people focused on uniquely human insight.

In the coming years, we expect “Ops Engineering” to sit alongside product, design, and GTM engineering as a core discipline. The companies that adopt it early will simply run faster with fewer people. And wonder how they ever managed without it.