Written by Scott Rinebolt- Director of Software Development
Every company wants to do something meaningful with AI right now. Far fewer know who should actually own that work.
The instinct is to post a job for a machine learning engineer or AI developer and assume technical horsepower is the whole job. It is not.
The AI initiatives that actually ship and deliver value are usually led by an AI Specialist who can do two very different things: build the solution and understand why anyone should care.
Why One Skill Set Isn’t Enough
A purely technical hire can be risky in isolation.
Give a strong engineer a vague mandate to “add AI” to a product, and you may get an impressive prototype that nobody asked for. The model may have strong accuracy metrics, but it could be solving a problem the business does not actually have. The technical work may be excellent. The requirements gathering may not be.
A purely customer-facing hire creates the opposite problem.
They may excel at discovery calls, workflow analysis, and uncovering pain points. They can often identify promising AI opportunities. However, when it comes time to scope the build, they may not know what is technically realistic. They may struggle to distinguish a two-week prototype from a six-month infrastructure project. They may also overlook where a model is likely to fail in production.
As a result, promises get made that the technical team must later revise or absorb at significant cost.
The gap between these two failure modes is exactly where AI projects die: translation.
Someone has to sit between what the client needs and what is technically possible. Just as importantly, they must be credible in both conversations.
What This Role Actually Does Day to Day
The phrase “technical and customer-facing” gets used loosely. It helps to define what it means in practice.
A successful AI specialist combines technical expertise with strong customer-facing skills. They can move comfortably between technical discussions, client conversations, and business strategy without losing credibility in either room.
The Technical Side
This person does not need to be the deepest AI researcher in the company. They do need real, hands-on fluency.
They should be able to prototype with current model APIs, evaluate data quality, and understand pipeline constraints. They should know the tradeoffs between a fine-tuned model, a retrieval-augmented generation (RAG) system, and a prompt-engineered workflow. They also need an honest understanding of what can be accomplished in a sprint versus a quarter.
Most importantly, they should be able to participate meaningfully in a technical design review rather than simply observe it.
The Customer-Facing Side
On the client side, this person needs to run a discovery conversation without leading the witness.
That means understanding the current process before proposing a solution. It means identifying who actually owns the problem rather than who simply attends the meeting. It also means gathering enough information about volume, frequency, and cost to build a realistic ROI case.
Just as important, they should be comfortable saying when AI is not the right answer. In many cases, that honesty is one of the fastest ways to build trust.
The Real Value: Translation
The connective tissue between these two skill sets is translation.
This person turns a business problem into a scoped technical approach. They also turn technical risks and constraints into language that business stakeholders can understand and evaluate.
Where to Find Candidates
This combination of skills is rare enough that searching by job title alone rarely works.
Solutions Engineers and Sales Engineers
Solutions engineers and sales engineers from technical software companies deserve serious consideration.
They already operate at the intersection of technology and business. Many are also looking to move deeper into AI as the market evolves.
Technical Consultants
Technical consultants from implementation-focused firms can be another strong source.
Unlike pure R&D professionals, they typically have experience scoping projects, managing client expectations, and delivering outcomes. That cycle closely mirrors what this role demands.
Startup and Founder Backgrounds
Engineers who have spent time at early-stage startups often develop these skills out of necessity.
Small teams force people to wear multiple hats. They build, sell, scope, and troubleshoot. While that experience may not appear clearly in a job title, it often becomes obvious in conversation.
Internal Talent
Before launching an external search, consider your own organization.
Someone who already understands the product and customer base may have a meaningful advantage. Even if they need additional AI training, they start with valuable institutional knowledge that an outside hire lacks.
How to Evaluate for the Role
Most interview processes only test one side of this job.
A coding exercise measures technical ability. A behavioral interview may reveal communication skills. Neither tests the most important capability: moving between technical and business conversations.
A more useful approach is a paired exercise.
Present the AI specialist candidate with a realistic but underspecified client scenario. Ask them to conduct a 15-minute discovery conversation. Then have them sketch a technical approach, including potential limitations and failure points.
Pay attention to whether they ask clarifying questions before suggesting solutions. Also watch how they respond when new information appears mid-exercise, such as an unexpected data constraint.
Their ability to adapt is often more revealing than their initial answer.
It is also worth asking about a time when a client’s expectations diverged from the technical reality of a project. AI specialist Candidates who have truly operated at this intersection usually have a detailed story rather than a generic response about communication.
Compensation and Search Expectations
Because this is a hybrid role, benchmarking against a single job title often leads to underpricing.
The position should be valued more like a senior technical lead with a client-facing premium. It should not be treated as a generalist or junior AI coordinator role.
Undervaluing the position is one reason searches drag on. The job description attracts mid-level candidates, while stronger candidates see a mismatch and move on.
Organizations should also expect the hiring process to take longer than a typical technical search. That delay is not necessarily a sign of a broken process. It is simply a reflection of how uncommon this skill combination is.
Resist the temptation to hire someone who is only strong on one side of the role with the assumption that the other half can be learned later. In practice, the translation skill is much harder to develop from scratch. Technical knowledge is generally more teachable.
Why This Hire Matters
The reward for getting an AI specialist hire right extends far beyond a single role on an org chart.
This is often the person who determines whether an AI initiative becomes a trusted, revenue-generating solution or an impressive demo that stalls after the first meeting.
The difference is rarely the technology itself.
More often, it comes down to whether someone can bridge the gap between what customers need and what AI can realistically deliver.