How do you deal with encountering “basic” Python functions you’ve never seen while solving

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Wiame El Korno

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I’m early in my career and would really appreciate some guidance from experienced professionals in AI/ML.

For my final year project, I focused on ML engineering—training models, exploring architectures, and working hands-on with tools like PyTorch. However, in my current (first) job, the work is centered around building GenAI/LLM applications using APIs like OpenAI and Gemini. It’s more about integrating existing models than developing or training them.

While the work is interesting, I feel uncertain about my growth. I’m not gaining deep technical experience or strengthening my core ML skills. Long-term, I’d like to either:

Pursue a job abroad (e.g., in Europe), or
Apply for a master’s in AI/ML with scholarship support.

I’m currently torn between three options:

Continuing with LLM application development (e.g., agents, API-based tools),
Shifting toward core ML engineering (research, training models), or
Trying to balance both.

If anyone has faced a similar crossroads or has insights into which path offers better learning and international opportunities, I’d be grateful for your advice.

Thanks in advance!

<p data-start="129" data-end="234">I’m early in my career and would really appreciate some guidance from experienced professionals in AI/ML.</p>
<p data-start="236" data-end="587">For my final year project, I focused on ML engineering—training models, exploring architectures, and working hands-on with tools like PyTorch. However, in my current (first) job, the work is centered around building GenAI/LLM applications using APIs like OpenAI and Gemini. It’s more about integrating existing models than developing or training them.</p>
<p data-start="589" data-end="762">While the work is interesting, I feel uncertain about my growth. I’m not gaining deep technical experience or strengthening my core ML skills. Long-term, I’d like to either:</p>
<ul data-start="764" data-end="867">
<li data-start="764" data-end="809">
<p data-start="766" data-end="809">Pursue a job abroad (e.g., in Europe), or</p>
</li>
<li data-start="810" data-end="867">
<p data-start="812" data-end="867">Apply for a master’s in AI/ML with scholarship support.</p>
</li>
</ul>
<p data-start="869" data-end="910">I’m currently torn between three options:</p>
<ol data-start="912" data-end="1093">
<li data-start="912" data-end="993">
<p data-start="915" data-end="993">Continuing with LLM application development (e.g., agents, API-based tools),</p>
</li>
<li data-start="994" data-end="1066">
<p data-start="997" data-end="1066">Shifting toward core ML engineering (research, training models), or</p>
</li>
<li data-start="1067" data-end="1093">
<p data-start="1070" data-end="1093">Trying to balance both.</p>
</li>
</ol>
<p data-start="1095" data-end="1256">If anyone has faced a similar crossroads or has insights into which path offers better learning and international opportunities, I’d be grateful for your advice.</p>
<p data-start="1258" data-end="1276">Thanks in advance!</p>

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Yasir 18 hours ago

Here’s my take as someone who’s navigated similar career decisions:

Go with option 3 (balance both), but be strategic about it.

Your current GenAI/LLM role is actually more valuable than you might think. Companies abroad are actively seeking people who can bridge the gap between research and practical AI applications. The ability to build production-ready AI systems is highly sought after in Europe’s tech scene.

For international opportunities: Your LLM application experience is a strong differentiator. European companies are investing heavily in AI products, and they need people who understand both the technical and practical sides.

For master’s applications: Admissions committees love candidates who can demonstrate real-world impact alongside technical depth. Your current work provides the “impact” story, but you’ll need to supplement with core ML projects.

My recommendation:

Stay in your current role but negotiate time for research projects
Contribute to open-source ML frameworks (PyTorch, Hugging Face)
Pick 1-2 core ML areas (computer vision, NLP research) for deep-dive side projects
Document everything—both your production AI work and research experiments

Timeline: Give yourself 12-18 months to build this hybrid profile before applying for masters programs. The combination of production experience + research contributions will make you incredibly competitive for both scholarships and international opportunities.

The key is positioning yourself as someone who understands both cutting-edge research and real-world deployment challenges.

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<p class="whitespace-normal break-words">Here's my take as someone who's navigated similar career decisions:</p><br />
<p class="whitespace-normal break-words"><strong>Go with option 3 (balance both), but be strategic about it.</strong></p><br />
<p class="whitespace-normal break-words">Your current GenAI/LLM role is actually more valuable than you might think. Companies abroad are actively seeking people who can bridge the gap between research and practical AI applications. The ability to build production-ready AI systems is highly sought after in Europe's tech scene.</p><br />
<p class="whitespace-normal break-words"><strong>For international opportunities:</strong> Your LLM application experience is a strong differentiator. European companies are investing heavily in AI products, and they need people who understand both the technical and practical sides.</p><br />
<p class="whitespace-normal break-words"><strong>For master's applications:</strong> Admissions committees love candidates who can demonstrate real-world impact alongside technical depth. Your current work provides the "impact" story, but you'll need to supplement with core ML projects.</p><br />
<p class="whitespace-normal break-words"><strong>My recommendation:</strong></p><br />
<ul class="[&:not(:last-child)_ul]:pb-1 [&:not(:last-child)_ol]:pb-1 list-disc space-y-1.5 pl-7"><br />
<li class="whitespace-normal break-words">Stay in your current role but negotiate time for research projects</li><br />
<li class="whitespace-normal break-words">Contribute to open-source ML frameworks (PyTorch, Hugging Face)</li><br />
<li class="whitespace-normal break-words">Pick 1-2 core ML areas (computer vision, NLP research) for deep-dive side projects</li><br />
<li class="whitespace-normal break-words">Document everything—both your production AI work and research experiments</li><br />
</ul><br />
<p class="whitespace-normal break-words"><strong>Timeline:</strong> Give yourself 12-18 months to build this hybrid profile before applying for masters programs. The combination of production experience + research contributions will make you incredibly competitive for both scholarships and international opportunities.</p><br />
<p class="whitespace-normal break-words">The key is positioning yourself as someone who understands both cutting-edge research and real-world deployment challenges.</p>

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Arjun on June 7, 2025

You’re in a great spot early in your career—exposure to both LLM applications and core ML is valuable. If your long-term goals involve deeper technical growth or grad studies abroad, building a stronger foundation in core ML (training models, understanding architectures, working with data at a lower level) will help you stand out more for research roles and scholarships.

That said, don’t underestimate the value of GenAI/LLM work—it’s highly relevant right now and offers real-world product experience. If possible, try to balance both: keep your job but carve out time for side projects or open-source contributions focused on training models or experimenting with ML systems. This hybrid approach keeps you professionally relevant while building the depth needed for future opportunities.

So, lean into your strengths from your final year project, keep learning on the side, and consider applying for research internships or fellowships abroad to strengthen your grad school profile.

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<p data-start="0" data-end="373">You're in a great spot early in your career—exposure to both LLM applications and core ML is valuable. If your long-term goals involve deeper technical growth or grad studies abroad, building a stronger foundation in core ML (training models, understanding architectures, working with data at a lower level) will help you stand out more for research roles and scholarships.</p><br />
<p data-start="375" data-end="794">That said, don’t underestimate the value of GenAI/LLM work—it’s highly relevant right now and offers real-world product experience. If possible, try to balance both: keep your job but carve out time for side projects or open-source contributions focused on training models or experimenting with ML systems. This hybrid approach keeps you professionally relevant while building the depth needed for future opportunities.</p><br />
<p data-start="796" data-end="990" data-is-last-node="" data-is-only-node="">So, lean into your strengths from your final year project, keep learning on the side, and consider applying for research internships or fellowships abroad to strengthen your grad school profile.</p>

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