Career Dashboard
Current Target Role: AI Solutions Engineer (Tentative)
Original Time-to-Hire Estimate: 3–6 Months
Current Time-to-Hire Estimate: 3–5 Months
Confidence Level: Medium
Remaining Skill Gaps:
- Practical AI project experience
- Cloud deployment experience
- Portfolio evidence
- Better understanding of AI-adjacent roles
Today’s Question
How much of my previous experience is actually transferable? Three days into this experiment, I’ve noticed something unexpected. The more job postings I review, the less convinced I become that this transition is primarily a learning problem.
At first glance, that statement sounds ridiculous. Of course I need to learn new things. AI wasn’t a major part of my professional life. Cloud platforms have evolved significantly. Entire categories of technology roles barely existed when I started my career. Yet I keep finding myself returning to the same question.
What if I’m underestimating the value of experience I’ve already accumulated?
What I Worked On
Most of today was spent comparing different technology career paths rather than researching specific technologies.
On Day 1, I was primarily trying to understand the market.
Today, I was trying to understand myself within that market. Those are related questions, but they’re not the same. I created a simple comparison between several roles that appeared repeatedly during my research:
- AI Engineer
- Machine Learning Engineer
- Solutions Engineer
- Technical Consultant
- Platform Engineer
- Product Owner
- Technical Account Manager
Rather than looking at technologies, I focused on responsibilities.
What does the person actually do all day?
What problems are they expected to solve?
What skills seem difficult to teach?
What experiences seem difficult to replace?
The exercise produced a surprising result. Many of the responsibilities described in Solutions Engineering and Technical Consulting roles felt familiar. Not identical to my previous work, but familiar enough that I could see the connection.
Troubleshooting.
Understanding complex systems.
Communicating with stakeholders.
Translating technical concepts into business language.
Managing competing priorities.
Investigating production issues.
Documenting processes.
These weren’t things I hoped to learn. These were things I had already spent years doing.
What I Learned
One concept that kept appearing during my research was the distinction between building AI systems and helping organizations adopt AI systems.
Before starting this experiment, I didn’t really appreciate the difference. When most people hear the phrase “AI career,” they tend to imagine someone training machine learning models, conducting research, or building advanced algorithms.
Those roles certainly exist.
However, I found a growing number of positions focused on helping businesses implement and use AI technologies rather than creating the underlying models themselves.
That distinction led me to spend some time investigating the role of an AI Solutions Engineer.
At a high level, an AI Solutions Engineer sits somewhere between engineering, consulting, and customer success. Their job is often less about inventing technology and more about helping organizations solve business problems using available technology.
That might involve:
- Understanding customer requirements
- Designing technical solutions
- Creating proof-of-concept implementations
- Explaining technical tradeoffs
- Working with engineering teams
- Supporting deployments
What interested me wasn’t the title itself. It was the skill profile.
The more I examined these positions, the more I noticed that many of the required skills weren’t purely technical.
Communication appeared repeatedly.
Problem-solving appeared repeatedly.
Stakeholder management appeared repeatedly.
Domain knowledge appeared repeatedly.
Technical depth was still important, but it wasn’t the entire job. That realization felt significant.
For the first time since beginning this experiment, I started to see a plausible path that didn’t require discarding everything I had learned over the last decade. I also spent some time learning about Retrieval-Augmented Generation, commonly referred to as RAG.
The term appeared frequently in AI-related discussions, so I wanted to understand what it actually meant.
At a simplified level, RAG is a technique that allows an AI model to access external information before generating a response. Rather than relying solely on what was learned during training, the system retrieves relevant information from a knowledge source and incorporates it into the response process. A practical example might be a company chatbot that answers questions about internal policies. Instead of training a custom AI model from scratch, the system can retrieve information from existing documentation and provide answers based on that content.
At this stage, I certainly don’t know how to build sophisticated RAG systems. What I gained was conceptual understanding.
More importantly, I began to understand why employers care about it. Many business AI applications appear to involve connecting existing models with existing business information. That’s considerably different from building AI from scratch.
Market Observations
A pattern is beginning to emerge. The technology industry appears to place a premium on people who can bridge gaps.
The gap between business and technology.
The gap between customers and engineering teams.
The gap between complex systems and practical outcomes.
Interestingly, those bridging roles often seem harder to automate than purely technical tasks.
- AI can generate code.
- AI can summarize documents.
- AI can answer questions.
But understanding organizational politics, customer expectations, stakeholder priorities, and business constraints remains considerably more difficult. That observation may ultimately prove important.
Resources Reviewed
- AI Solutions Engineer Job Postings
Why I chose them: The role kept appearing during my research despite not being part of my original plan.
What I learned: Many positions value communication and technical breadth as much as deep specialization.
Was it worth it? Yes. This was probably the most influential discovery of the day. - Articles Explaining Retrieval-Augmented Generation (RAG)
Why I chose them: The term appeared frequently in AI-related job descriptions.
What I learned: Many practical AI systems focus on integrating information sources rather than training models.
Was it worth it? Yes. It helped demystify part of the AI landscape.
Progress Against Plan
Slightly ahead. I expected to spend much longer evaluating potential target roles before finding a direction worth exploring. While no final decision has been made, AI Solutions Engineering has emerged as a strong candidate.
Strategy Changes
The target role has shifted from “Undecided” to “AI Solutions Engineer (Tentative).” The word tentative is important. This is not a commitment.
It is a hypothesis.
The next phase of the experiment will test whether that hypothesis survives contact with reality.
Next Steps
Over the next few days, I plan to move beyond job descriptions and begin examining the actual skills required to perform these roles.
I also want to build something small. Not because I believe projects automatically create opportunities, but because hands-on work often reveals gaps that research cannot. At some point, reading must give way to doing.
Reflection
Three days ago, I approached this transition as if I were standing at the bottom of a mountain. Everything felt unfamiliar.
The assumption was that I needed to climb an entirely new discipline from scratch. Today, I’m beginning to wonder whether that assumption was wrong.
The technology is certainly changing. The market is certainly changing.
But experience doesn’t simply disappear because new technologies emerge. The challenge may not be replacing my previous experience. The challenge may be figuring out where that experience still matters.
At the moment, that feels like a much more interesting question.
