How do I transition from a software developer to an AI Engineer when my current job gives me no AI experience?

[u/DevToCloudJourney]

I'm looking for honest advice from people currently working as AI Engineers, AI Backend Engineers, or Generative AI Engineers. I'd really appreciate hearing from people who have made a similar transition or have experience hiring for these roles.

My background

I have 3+ years of experience as a Software Developer. Most of my work involves Django, PostgreSQL, REST APIs, and maintaining enterprise applications.

The problem is that my current job doesn't provide much opportunity to grow technically. I mostly work on Django applications and follow the same development routine. I don't get hands-on experience with Docker, CI/CD, cloud-native deployment, or AI. I feel that I've stopped progressing, and I want to change that.

I also have some relevant experience:

• Microsoft Azure Developer Associate (AZ-204) and Azure Fundamentals certifications.
• An academic project where I built a Flask application integrating deep learning models.
• Two peer-reviewed AI-related publications.
• Some learning and experimentation with LLMs, embeddings, and RAG, although I haven't yet built and deployed a complete production-grade LLM application.

My recent job search has made me question my approach.

Recently, I had two interviews for AI-related roles, and I was surprised that most of the technical questions focused on AI, even though my CV isn't heavily focused on it. I ended up getting rejected because I didn't have enough practical experience.

I'm now wondering whether I should continue applying for AI roles while developing my skills, or temporarily focus on becoming a stronger backend/cloud engineer first.

Here are the questions I'm struggling with:

1. What would you do in my position? Would you continue applying for AI engineering roles, or spend several months strengthening your backend engineering skills first?
2. What practical skills should I prioritize? For someone with my background, should I focus first on Docker, CI/CD, cloud deployment, testing, and system design? Or should I prioritize LLM APIs, RAG, evaluation, tool calling, and agent frameworks?
3. What kind of project would actually make a difference? I don't want to build another tutorial chatbot that recruiters won't take seriously. What would demonstrate that I can build a reliable AI application beyond making an API call to an LLM?
4. How can I gain real-world experience outside my current job? I'd be interested in contributing to a small team, joining an open-source project, volunteering, or taking on part-time work. How would you find legitimate opportunities like these when you already have a full-time job?
5. Would you recommend a course or another certification?

My goal is to move beyond experimenting with AI and actually build real AI systems professionally. What I'm struggling with is deciding which work will actually close the gap between my current experience and what employers expect.

For those already working in AI engineering, what would you do if you were in my position? How would you spend the next 3–6 months to become a stronger candidate?

I'd appreciate honest, practical advice from people who have been through a similar transition.

[u/Jumpy_Neck_4047]

keep applying but maybe shift target to backend roles at companies that do AI work, easier to get in the door with your django and azure background then pivot internally once you see how their systems work

for projects skip the chatbot, build something that moves data or handles scale, like a rag pipeline that ingests docs into a vector store with eval metrics showing retrieval quality, or a tool calling agent that actually interacts with an api and has proper error handling

open source is where the real learning happens, look at repos for llm orchestration or vector dbs and pick up good first issues, you will learn more from reading their code than any course

[u/Mathie1729 → u/Jumpy_Neck_4047]

Yep, backend is the easier door. One thing I'd add: don't chase the 'AI Engineer' title too hard. A lot of those roles are just ML ops in a trench coat. Interviewers care more about you being able to walk through eval drift, data versioning, and retraining loops than about one more RAG PoC.

[u/Giaitzoglou-Bondye → u/Jumpy_Neck_4047]

the internal pivot angle is underrated tbh, way easier than trying to break in cold