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Agentic AI Engineering

900 000 ₸
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Allocated 11 Quotas

Agentic AI Engineering is a 26-week course designed for learners aiming to become AI engineers from scratch. No prior experience is required — the course starts with Python programming, working with data, and basic machine learning. Then you progress to building AI agents using the Vibe Coding philosophy: you define tasks, and AI tools generate the code and solutions. The program covers full AI engineering: creating single and multi-agent systems, RAG pipelines, LLM fine-tuning, deployment, and AI safety. By the end of the course, students develop a production-ready AI system and defend it in front of industry experts.

Special condition

Student Selection Criteria: Stage 1 — Application Submission: The participant fills out an online form with their CV. Stage 2 — Application Review: The Outpeer committee selects candidates. Stage 3 — In-depth Interview: Selected candidates participate in a 20-minute online video interview. Stage 4 — Pre-study: Self-study of preparatory material before the main course starts. Other Conditions: Full course fee: 900,000 KZT Students receiving the TechOrda grant pay an additional 500,000 KZT — the rest is covered by the grant If a student is on the waitlist, Outpeer covers the remaining fee Failure to pass the final test at the end of the course results in a penalty of 400,000 KZT Payment installment and discount terms are clarified with the course manager Waitlist: To join the waitlist, the student pays 500,000 KZT When a spot becomes available, the student is automatically transferred to the TechOrda grant The 500,000 KZT paid is counted as part of the grant contribution

Course details

level

For all

Study format

Online

Entrance exams

No

Duration, in weeks

26

Education language

Russian

Qualifications

junior

Skills


Build and deploy ML models via API (FastAPI, Docker) Work with PyTorch, Transformers, and classical ML algorithms Create effective prompts and evaluate LLMs Develop RAG systems with vector databases Design AI agents with memory and LangGraph Build multi-agent systems (hierarchical, supervisor/worker) Fine-tune LLMs (LoRA/QLoRA) Set up MLOps/LLMOps pipelines and monitoring Ensure AI system safety (guardrails, prompt injection protection)

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