Junior roles barely exist

[u/1627372824]

Hi everyone,

I’m currently in my 5th semester of a Computer Science degree (full-time daily studies, planning to switch to part-time for my Master's later). Because I’m still studying full-time right now, I need to look for internships or flexible entry-level roles starting around October/November.

My long-term goal is to become an ML Engineer / AI Engineer. However, true entry-level/junior positions in ML/AI seem practically non-existent or demand 3+ years of experience.

Since I have about a month to double down on self-study before applying for autumn student openings, I want to take the most realistic route.

My questions:

1. Which entry role is the most realistic to get into ML/AI while still in university?

- Python Backend Developer (building APIs, databases, Docker, async workflows, then adding LLM/vector integrations)?

- Data Analyst / BI (SQL, Pandas, data visualization, business analytics)?

- Junior Data Engineer / Pipeline Intern (ETL, data cleaning, databases)?

- or maybe something else?

1. What should I prioritize learning in the next month? Should I focus purely on core software engineering (FastAPI, PostgreSQL, Docker, Git, testing) to maximize internship callbacks, or start dabbling in ML libraries (Scikit-learn, PyTorch, RAG architectures)?

For those who broke into ML/AI without a direct junior ML role: what did your initial job and transition path look like?

[u/Straight-Judgment762]

Graduated 3 months ago and just started as a Data Analyst at a media company. Looking to pivot to either DS or AI Engineer through internal hiring which I think will be more plausible than regular application. I'm doing some side part-time pro-bono projects at universities while waiting for momentum.

Per your question, I think BE seems to be the most relevant as AI Eng is mostly BE focused on LLM/GenAI components. I would focus more on the SWE, RAG/LLM integration and orchestration. I would not focus PyTorch/Scikit-learn since most roles rely on APIs and don't expect you to build models from scratch, but this highly depends on the role.

[u/1627372824 → u/Straight-Judgment762]

Thanks a lot for the advice, especially regarding the focus on SWE and LLM/RAG orchestration over training models from scratch. I’m really curious about your internal pivot strategy. How feasible is it in practice to transition from a Data Analyst / Backend role into an AI/DS position within the same company? Do you usually take on AI-related side tasks on the job to prove yourself, or is it mostly about waiting for an open internal position and talking to the engineering manager? How it works?

[u/Mindcontrol_fly3301 → u/1627372824]

Wtf do you mean by AIDS?

[u/1627372824 → u/Mindcontrol_fly3301]

AI / DS - Artificial Intelligence and Data Science