Reader,

Let's recap.

In Part One, we saw how a vague, canned requisition left a sales rep struggling to do 2 things:

• align with the hiring manager
• define a clear candidate profile

He'd send resume after resume to the hiring manager as the candidate profile was constantly changing.

It became a fool's errand trying to get the right candidate for the role.

The breakthrough came when an engineer joined the process.

Their perspective didn’t just clarify technical requirements. It gave the rep the tools to:

• have productive conversations
• spot gaps the manager couldn’t articulate
• guide the discussion toward a clear candidate profile

Without that insight, the rep would still be playing guessing games, slowing down the process.

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In this installment, we’ll break down how we analyzed the requisition from an engineering perspective:

• Identifying what this role is actually being hired to build
• Locking down the technical background required to deliver it

This step turns confusion into clarity.

It equips recruiters to confidently guide the client even when requirements are shaky, and helps the rep focus on what really matters: closing the role.

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Let's take a look at this Goldman Sachs req for an AI Developer:

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The big things this req asks for are:

• Strong enterprise software development experience
• Hands-on exposure to modern AI tooling and LLMs
• Experience integrating LLMs into production workflows
• Comfort with AI frameworks like LangChain, Hugging Face Transformers

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But the bullet points in the job req have problems. Too much is left open to interpretation.

• “Experience with AI tools post-2022” → Too broad. Does this mean they want someone who has just experimented with ChatGPT, an expert at prompt engineering, or someone who has built production-level solutions using multiple frameworks? Yeah, I'm not sure either.
• “Enterprise software development experience” → Could be interpreted as legacy systems only; does it include cloud-native architectures, microservices, containerized deployments?
• “Hands-on with LLMs” → Is this integrating APIs, fine-tuning models, or building AI agents from scratch? Those are very different skills, each potentially valuable to a role calling for "hands-on LLM experience".

These bullets are vague, but that vagueness is often intentional in canned requisitions.

(Hold on, I didn't say "intentional" meant "good")

Often, vague bullet points like these are intended to capture multiple roles at once (e.g., ML engineer, AI developer, AI researcher).

If you're looking for a unicorn or if any of the skill sets will do, that might make sense. But that's something you want to clarify.

Without clarifying, sales reps will probably waste time on the wrong candidates.

Areas to Push Back and Clarify

You might be asking yourself, how do I clarify the ask?

How do you make sure the candidates you get for this req are the ones the team will want to hire?

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Consider these angles:

1. Project & Team Scope
   ​• Are we building a new AI initiative, supporting existing systems, or delivering production client-facing solutions?
   • Who are the stakeholders?
   • Will the hire work solo or in a larger AI/ML team?
   ​• Growth trajectory: Is this a one-off hire or the start of an AI function?
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2. Technical Requirements​
   ​Model specificity vs architecture:Must the candidate have experience with a specific LLM like ChatGPT or Gemini, or is the ability to architect and switch between models more critical?
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   ​Timeline of experience: Are they expecting hands-on LLM experience post-2022, or are strong foundational software/ML skills sufficient? If not, why not (let's understand how the manager thinks).
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   ​Coding vs low-code tools: Are low-code frameworks like N8N, Zapier, or Make relevant, or is full-stack Python/Java expertise required?
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3. Data & Infrastructure​
   ​State of the data: Is the candidate responsible for cleaning/engineering data, or is that handled elsewhere?
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   ​Operational constraints: What inputs/outputs, APIs, or pipelines are expected?
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4. Communication & Stakeholder Management​
   • Is this hire expected to bridge business and technical teams, or mostly focus on coding?
   • Should the role be framed as AI Engineer or AI Solution Architect based on interaction needs?

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Technical literacy is simply a non-negotiable here if you want to be a strategic partner.

To know what questions to ask, you need to know enough about AI to know that this req was too generic. And you need to know what was generic about it.

But you're not going to learn everything about AI in this email.

What you can take away from this is a few solid questions worth asking the hiring manager about an AI Developer req like this.

Sample Questions for the Hiring Manager

See if you can pick up on the patterns:

• What are the immediate deliverables and business objectives driving this hire?
• Is the expectation to deploy, develop, fine-tune, or integrate AI tools?
• Are primary tasks internal process improvement, external product delivery, or R&D?
• Is this role focused on building and training models, or on orchestrating AI tools into workflows and applications?
• Are we hiring someone to create the intelligence, or someone to apply and operationalize it inside the business?
• Which LLM frameworks or agents are most important and why?
• If low-code tools are referenced, which are currently in use and are alternatives acceptable?

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Clarifying these details ensure you stop wasting time with unqualified candidates.

Technical literacy is required, but precision in requirements and understanding client adoption is what positions you as a trusted advisor.

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You already know how to navigate tough software engineering searches.

You've dealt with picky managers.

AI just makes it messier. A lot messier.

Especially if you don't understand AI well enough qualify those reqs.

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Understanding what each bullet point really means and when to push back on the decision maker is the difference between being reactive and guiding the client with authority.

I'm willing to bet there's at least 1 req on your desk right now that could use some TLC.

What could you ask the hiring manager to get the best possible candidates on the first go?

Additional Tactics:

My advice always depends on a few factors, and one of the biggest is the state of your relationship.

In this example, we are working with a manager who is new to the rep. Depending on how important the account is, I might recommend a few additional moves:

• Introduce your engineer to the hiring manager to create a hiring rubric, especially if there are multiple openings tied to Q1 projects.
• Map the department & building rapport directly with the engineering team. Let's hear about the environment and builds from folks with boots on the ground. If there are other contractors, it's important we know who they are and which firm they're from.
• Set expectations early around communication, feedback, and how candidates will be evaluated.
• Plan on leveraging your first placement.

These steps do not just improve this one search. They set the foundation for a smoother, more predictable hiring motion as the account grows.

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Let us know!

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-Steven