Most mid-sized employers are past the pilot stage with AI. Someone ran a proof of concept, it worked in a sandbox, and now leadership wants it running in the real business. That is where projects stall, and the cause is rarely the software. It is the team around it.
This applies well beyond telecom. Utilities, logistics, manufacturers, distributors and financial services firms all run on systems that were built long before anyone talked about models and agents. The hiring question is the same in each: who connects the new tools to the old ones?
The gap sits between two kinds of expertise
You will find plenty of candidates who know modern data tooling. You will find plenty of long-tenured people who know exactly how your plant floor, billing system or dispatch network behaves. Very few people are strong at both.
When a rollout fails, the pattern is usually one of these:
- The AI hire builds something clean that does not fit how the work is actually done.
- The operations team does not trust the output, so they route around it.
- Nobody owns the handoff, so data quality problems sit unresolved for months.
- The model works, but no one is accountable for what it does once it is live.
None of those are technical failures. They are staffing and role design failures.
Roles worth defining before you post anything
If you are moving AI into production, think about these functions rather than a single job title:
- Data engineer with operational exposure. Someone who can clean and move data out of older systems without breaking the processes that depend on them.
- Applied AI or machine learning engineer. Focused on deployment, monitoring and maintenance, not research.
- Operations lead who owns the workflow. Often an existing employee, given time and authority to shape how the tool is used.
- Security and governance owner. Responsible for access, audit trails and what the system is allowed to do without a person approving it.
- Business analyst or product owner. Translates between the people doing the work and the people building the tool.
In a smaller company, one person may cover two of these. Be honest about which two, and about what you are not covering.
Contract first, permanent second
Many AI projects have a build phase that is intense and finite, followed by a steady maintenance phase. Hiring permanently for the peak is expensive. Hiring only contractors for the whole thing leaves you with no one who understands the system after they leave.
A workable split:
- Bring in contract specialists for data migration, integration and initial deployment.
- Hire permanent staff for ownership, monitoring and ongoing improvement.
- Require documentation and a structured handover as part of every contract engagement.
- Pair each contractor with an internal person who will inherit the work.
That pairing is the part most employers skip, and it is the part that determines whether the investment lasts.
Security is now part of the job description
As more of your operation runs through AI tools, those tools become something to protect. Employers are increasingly asking how models are accessed, what data they see and how outputs are checked. That belongs in hiring criteria, not as an afterthought. Ask candidates how they have handled access controls, logging and human review on a live system. Someone who has only built demos will struggle with those questions.
Screen for translation, not just technical skill
In interviews, give candidates a real scenario from your business. Describe a messy process, an older system and a goal, then ask how they would approach it. Strong candidates ask about the people doing the work, the exceptions and the failure cases before they talk about tools. Weaker ones jump to the technology.
Also ask what they would leave alone. People with real deployment experience know that not every process should be automated, and they can say why.
Pay and timing
Candidates with both data skills and operational experience are in short supply across Canada and the US, and they know it. If your offer is pegged to a general IT band, you will lose them to employers who priced the role correctly. Review compensation against the combined skill set, and move quickly once you have a good conversation. Long processes cost you the best people first.
What to do next
Before opening a requisition, write down the one workflow you want AI to improve, who does that work today, and which of the five functions above you already have in-house. The gaps are your hiring plan. Start with the one that is blocking progress, and decide whether it is a contract need or a permanent one.
If you want a second opinion on role design or need to find people who can bridge older systems and new tools, a Traverse recruiter can help.