Career Dashboard
Current Target Role: Undecided
Original Time-to-Hire Estimate: 3–6 Months
Current Time-to-Hire Estimate: 3–6 Months
Confidence Level: Medium
Remaining Skill Gaps:
- Understanding current technology hiring market
- Identifying realistic target roles
- Determining which existing skills remain valuable
- Understanding AI-related opportunities and limitations
Today’s Objective
Before learning anything, I wanted to answer a simple question.
What am I actually trying to become?
That may sound obvious, but after a layoff, it is surprisingly easy to confuse activity with progress. There is no shortage of AI courses, cloud certifications, coding bootcamps, YouTube tutorials, and LinkedIn posts insisting that a particular technology is the future.
The problem is that none of those things matter if they do not lead to employment.
This experiment is supposed to optimize for employability, not excitement. So rather than immediately jumping into another course, I spent the first day trying to understand the market.
My assumption going into this exercise was straightforward. AI seems to be everywhere. Every technology conference is talking about it. Every social media platform is discussing it. Every company appears to be announcing some sort of AI initiative.
Naturally, I assumed AI would be the most logical destination.
By the end of the day, I wasn’t so sure.
What I Worked On
Most of my time was spent reviewing job postings rather than learning new technology.
I looked at openings across several categories:
- AI Engineer
- Machine Learning Engineer
- Software Engineer
- Platform Engineer
- DevOps Engineer
- Solutions Engineer
- Technical Consultant
- Product Owner
Rather than focusing on salary or company prestige, I concentrated on patterns.
What skills appeared repeatedly?
What experience seemed non-negotiable?
Which positions overlapped with my existing background?
Which roles appeared to require a complete restart?
The goal wasn’t to find a job yet. The goal was to identify which direction deserved further investigation.
What I Learned
The biggest surprise was discovering how different AI enthusiasm is from AI hiring.
Many AI-related job postings still expect candidates to possess years of software engineering experience, production deployment experience, cloud experience, and practical AI project experience.
In other words, most employers are not looking for people who recently became interested in AI.
They are looking for people who already have experience applying it.
This may seem obvious in hindsight, but it was an important distinction.
I also noticed something else.
Many organizations appear less interested in hiring people who can build large language models and more interested in hiring people who can apply them.
That led me to spend some time understanding a concept that appeared repeatedly in job descriptions: LLM APIs.
For anyone unfamiliar with the term, LLM stands for Large Language Model. Examples include the models behind ChatGPT and similar AI systems. An API, or Application Programming Interface, is a mechanism that allows software applications to communicate with other software services.
When combined, an LLM API allows developers to send requests to an AI model and receive responses programmatically.
At a basic level, this involves:
- Authentication
- Sending prompts
- Receiving responses
- Handling errors
- Managing usage costs
More advanced implementations involve:
- Function calling
- Retrieval-Augmented Generation (RAG)
- Multi-step workflows
- Agent systems
- Production monitoring
What stood out to me was that many employers are not asking candidates to train AI models.
They are asking candidates to integrate existing models into business workflows.
That feels significantly more attainable.
Perhaps more importantly, it feels adjacent to skills I already possess.
Throughout my career, much of my work involved understanding systems, troubleshooting complex issues, communicating with stakeholders, and connecting business requirements to technical solutions.
The technology may be changing, but some of the underlying problem-solving skills remain relevant.
That was encouraging.
Market Observations
If I had to summarize today’s findings in one sentence, it would be this:
The market appears to value experience more than I initially expected.
Before starting this experiment, I assumed my lack of practical AI experience would outweigh everything else.
Instead, I found myself repeatedly encountering positions where communication, troubleshooting, documentation, client interaction, and technical breadth appeared just as important as deep specialization.
That does not mean learning new skills isn’t necessary.
It absolutely is.
But it may mean that this transition is less about becoming someone new and more about repositioning existing experience.
That distinction could dramatically affect the strategy going forward.
Resources Reviewed
Job Postings
Why I chose them
Job postings represent actual employer demand rather than online opinions.
What I learned
Many AI positions require significantly more experience than social media discussions often suggest.
More importantly, several adjacent roles appeared to align closely with my existing background.
Was it worth it?
Yes.
This was probably the highest-value activity I could have done on Day 1.
Progress Against Plan
On track.
The objective was never to start learning immediately.
The objective was to avoid spending months learning the wrong thing.
Today’s findings suggest that taking time to understand the market first was the correct decision.
Strategy Changes
None.
It is too early to commit to a specific path.
However, I am less convinced that “AI Engineer” should automatically be the target role.
Next Steps
Over the next few days, I plan to continue evaluating different technology career paths and compare them against my existing experience.
I am particularly interested in understanding whether solutions engineering, technical consulting, or AI-focused customer-facing roles might offer a faster route back into the industry than attempting to compete directly for traditional AI engineering positions.
For now, the evidence remains incomplete.
Reflection
I started today believing the answer was probably AI.
I ended today believing the answer might be something adjacent to AI.
That may not sound like much progress, but I think it is.
Career transitions are often framed as learning journeys. Learn enough skills, build enough projects, and eventually opportunities appear.
What I am beginning to suspect is that this transition may be less about learning everything from scratch and more about identifying where existing experience remains valuable.
If that turns out to be true, it could significantly shorten the road ahead.
The next challenge is gathering enough evidence to know whether that assumption is correct.
