Career Dashboard
Current Target Role: AI Solutions Engineer
Original Time-to-Hire Estimate: 3–6 Months
Current Time-to-Hire Estimate: 3–5 Months
Confidence Level: Medium
Remaining Skill Gaps:
- Demonstrable AI project experience
- Cloud deployment experience
- Portfolio evidence
- Interview readiness
- Stronger positioning for target roles
Today’s Question
Am I missing skills, or am I presenting them poorly? Ten days into this experiment, I expected to be thinking primarily about technology. Instead, I’ve spent most of today thinking about language. Not programming languages.
Resume language.
Job title language.
Career narrative language.
The reason is simple. The more I investigate potential career paths, the more I realize that employers don’t evaluate my actual experience first. They evaluate a representation of my experience.
A resume.
A LinkedIn profile.
A project portfolio.
A brief conversation during a screening call.
Those things become proxies for everything I’ve done throughout my career. That realization led to an uncomfortable question. What if the biggest obstacle isn’t a skills gap? What if it’s a translation gap?
What I Worked On
Today, I stopped looking at job postings and started looking at myself through the eyes of a hiring manager. That sounds straightforward. It wasn’t.
Like most professionals, I tend to view my career through the lens of what I actually did.
I know the production incidents I helped resolve.
I know the systems I supported.
I know the stakeholders I worked with.
I know the responsibilities I carried.
A hiring manager doesn’t know any of those things. They only see what I communicate. To test this idea, I compared my existing experience against the responsibilities listed in AI Solutions Engineer and Solutions Engineering job postings. I wasn’t looking for missing technologies. I was looking for overlap.
What surprised me was how much overlap existed. Many postings emphasized:
- Problem-solving
- Technical communication
- Customer interaction
- Requirements gathering
- System troubleshooting
- Cross-functional collaboration
- Solution design
None of those responsibilities felt unfamiliar. In fact, they described a significant portion of my career. The problem was that my previous titles didn’t necessarily tell that story. That’s an important distinction.
Experience and positioning are related, but they are not the same thing.
What I Learned
One concept that became increasingly important today was solution design. The term appears frequently in Solutions Engineering and AI-related roles, but it’s not always clearly defined.
At a basic level, solution design is the process of determining how different technologies, systems, and processes can work together to solve a specific problem. The emphasis is not on individual technologies. The emphasis is on outcomes.
Imagine a company wants to provide employees with an internal AI assistant capable of answering policy questions.
The challenge isn’t simply selecting an AI model. The challenge involves understanding:
- Where the information lives
- How it will be accessed
- Security requirements
- User experience considerations
- System integration requirements
- Operational costs
The resulting solution might involve multiple technologies working together. That’s solution design. What struck me was how familiar that type of thinking felt.
Throughout my career, I have spent considerable time diagnosing issues, evaluating tradeoffs, coordinating stakeholders, and connecting systems together. I may not have used the term “solution design” regularly, but many of the underlying activities were already present. I also spent time reviewing portfolio projects created by professionals working in AI-adjacent roles.
Before doing this research, I assumed portfolio projects primarily existed to demonstrate technical competence. While that is certainly part of their purpose, I discovered something else. Many successful portfolio projects function as communication tools.
They show how someone approaches a problem.
They demonstrate reasoning.
They explain decisions.
The project itself matters.
The story behind the project matters just as much. That observation feels particularly relevant for someone pursuing a role that sits between technology and business. A portfolio isn’t simply proof that something works.
It’s evidence that I understand why it was built.
Market Observations
Over the last ten days, a pattern has continued to emerge. The market appears less interested in specialists who operate exclusively within one domain and more interested in people who can connect domains together. This trend appears repeatedly across different roles.
Solutions Engineers connect customers and engineering teams.
Product Owners connect business objectives and development priorities.
Platform Engineers connect infrastructure and application development.
Technical Consultants connect organizational needs and technical solutions.
Even many AI-related roles appear to focus on integration rather than invention. This observation doesn’t diminish the importance of technical skills. Technical credibility still matters. However, technical credibility alone may not be enough. Organizations seem to place increasing value on people who can translate complexity into action.
That’s a capability I may have underestimated at the beginning of this experiment.
Resources Reviewed
- Solutions Engineer Resumes and Portfolios
Why I chose them: I wanted to understand how experienced professionals present themselves to the market.
What I learned: Many successful candidates focus as much on outcomes and problem-solving as they do on technology stacks.
Was it worth it? Yes. This exercise revealed gaps in how I currently describe my own experience. - AI Solutions Engineering Portfolio Examples
Why I chose them: To understand what practical evidence employers might expect.
What I learned: Strong portfolios often emphasize decision-making and business impact rather than technical complexity alone.
Was it worth it? Absolutely. This may influence how I approach future projects.
Progress Against Plan
Slightly behind. The original expectation was that I would spend more time building and less time reflecting. However, today’s work uncovered something important. There is little value in building projects if I don’t understand how those projects support my broader career narrative.
Clarifying that narrative now may save time later.
Strategy Changes
A subtle but important change occurred today. Earlier in the experiment, I viewed projects primarily as learning exercises. I now view them as communication assets.
The goal is no longer simply to learn new technologies. The goal is to create evidence that supports a specific professional narrative. That distinction will likely influence which projects I choose to build moving forward.
Next Steps
The next phase of this experiment will focus on creating a portfolio project aligned with the AI Solutions Engineer path. I also plan to begin updating my resume and LinkedIn profile based on the patterns I’ve identified over the last ten days.
Most importantly, I want to start testing assumptions against reality. Research has value. Projects have value.
Eventually, neither matters unless they generate conversations with actual employers. At some point, applications need to be submitted.
Reflection
Ten days ago, I assumed the biggest challenge would be acquiring new skills. Today, I’m not so sure.
There are certainly skills I still need to develop. There are technologies I barely understand. There are concepts I haven’t explored yet. But the more I examine this transition, the more I suspect that the hardest part may be something else entirely. Translation.
Translating previous experience into current market language.
Translating technical knowledge into business value.
Translating years of work into a narrative that makes sense to someone who has never met me.
Perhaps that’s why career transitions feel so difficult. They aren’t simply exercises in learning. They’re exercises in interpretation. And right now, I feel less concerned about what I don’t know than I do about how well I can explain what I already do.
That’s not the realization I expected to reach after ten days. But it may be one of the most useful discoveries so far.
