u/Cautious_Low3118

I’m currently working in an AI/ML-focused role where I mostly work on AI integrations, APIs, full stack development, and some hands-on ML work. Planning my first switch soon for better pay and growth, and wanted to understand how interviews are usually conducted for candidates with ~1 YOE in this domain.

Wanted to know a few things from people already working in AI/ML:

• Do companies still ask aptitude rounds for experienced candidates?
• How much DSA is generally expected for AI Engineer / AIML roles?
• Are interviews more focused on ML concepts or engineering skills like backend, deployment, APIs, vector DBs, cloud, etc.?
• How different are startup interviews compared to MNCs?
• What should someone with ~1 YOE focus on the most before switching?

Would really appreciate any advice or interview experiences

I wanted some career advice from experienced AI/ML engineers regarding transitioning into an AI Engineer role with around 0–1 YOE.

I recently graduated and currently work in an AI/ML R&D team as an Associate Full Stack Developer. Most of my work has been around building and integrating AI systems rather than traditional frontend-heavy development.

Some of the things I’ve worked on:

• FastAPI-based AI microservices
• LLM pipelines using OpenAI/Grok APIs
• RAG and embeddings
• Sentence Transformers for semantic matching
• YOLOv8 computer vision models
• RabbitMQ event-driven pipelines
• OCR validation workflows
• Real-time Twilio + WebSocket AI call pipelines
• Basic predictive maintenance models (LSTM, Random Forest, HDBSCAN)

I understand AI/ML concepts fairly well and have hands-on implementation experience, but I sometimes feel I’m not “deep enough” in either full stack or core ML compared to dedicated specialists.

For people already working as AI Engineers:

1. How do early-career engineers usually position themselves during interviews?
2. During a first job switch, what matters most: DSA/coding rounds, ML fundamentals, system design, projects, or production experience?
3. What skills would you strongly recommend mastering for AI Engineer interviews in 2026?
4. Should someone like me focus more on:
   • DSA/coding practice
   • deeper ML theory
   • LLM/RAG systems
   • deployment/backend engineering
   • certifications
   • open-source contributions
5. How important are research papers and math-heavy ML knowledge for applied AI roles?

I’d genuinely appreciate honest advice on how to bridge the gap from “AI-integrated developer” to a strong AI Engineer profile.