Break Into AI Engineering — Build Real AI Apps, Get Recognized.
If you have built anything with LLM APIs, RAG pipelines, or AI agents — this internship is for you. Use OpenAI, LangChain, Ollama, or any AI framework you prefer. Complete the project, get recognized, and unlock access to paid AI engineering work.
Every valid submission is formally recognized with official Kagnus credentials.
A simple 5-step process — from application to your certificate and beyond.
Complete the application form with your personal details, academic background, preferred tech stack, and developer profiles. Takes approximately 2 minutes.
Our engineering panel reviews your profile. Shortlisted applicants receive an official Selection Letter and invitation to the domain-specific task group.
You'll receive a focused project brief matched to your domain. Build it at your own pace using any tech stack you prefer. Every candidate who submits a valid project receives an official Kagnus completion credential.
Top performers advance to a live technical interview with senior Kagnus engineers to evaluate architecture decisions and code quality.
Selected engineers join the Kagnus Engineering Network with access to paid work on real client projects, startup products, and commercial assignments.
Takes less than 2 minutes • No login required • We review every application within 3–5 business days
Complete the form below to submit your application. All fields marked with an asterisk are required. Typical completion time is under 3 minutes.
Answers to the most common questions about the Gen AI & AI Engineer internship at Kagnus.
Absolutely. You can use OpenAI, Anthropic, Google Gemini, or fully open-source models via Ollama, Hugging Face, or vLLM. We evaluate your architecture and implementation quality, not the model provider.
No. You can use free-tier APIs (OpenAI free credits, Gemini free tier), open-source models running locally via Ollama, or Hugging Face Inference API. The project is designed to work within free usage limits.
Not necessarily. We are looking for engineers who can build AI-powered applications — integrating LLMs via APIs, building RAG pipelines, and creating agent workflows. Deep ML research experience is a plus but not required.
RAG (Retrieval-Augmented Generation) is a technique that combines document retrieval with LLM generation for accurate, grounded responses. Familiarity with RAG concepts is strongly recommended for this domain.
Yes. Every candidate who submits a valid evaluation project receives an official Kagnus Completion Certificate. High performers who clear the interview receive enhanced credentials and access to paid AI engineering work.
The evaluation phase is an unpaid assessment. Engineers who advance through the interview and are selected into the Kagnus Engineering Network get access to paid AI/ML project work with real clients.
Yes. If you have built projects using LLM APIs, vector databases, or AI agent frameworks, you are welcome to apply regardless of your academic year or professional experience.
Yes, this is a fully remote opportunity. All project submissions, code reviews, and communication happen online. You can participate from anywhere with an internet connection.
