Beginner → Junior
AI Engineer
AI Engineer sits at the intersection of software engineering and LLMs. In this course you'll learn to ship applied AI products: chat assistants, RAG systems, AI agents. In 6 months you'll go from foundational Python to deploying an AI application with UI and API.
- 6 months / 26 weeks
- Online / Hybrid
- 400,000 ₸
- Beginner → Junior
Who this course is for
- Developers who want to move into AI
- Analysts and product folks who want to build AI products hands-on
- People with basic Python who want to work with LLMs
What you'll learn
- Working with LLM APIs (OpenAI, Anthropic) and advanced prompt engineering
- Building embeddings and working with vector databases
- Designing RAG pipelines: chunking, retrieval, ranking
- Building AI agents and integrating tool calling
- Evaluating answer quality and managing hallucinations
- Deploying an AI application with API and UI
Monthly program
- 1
Python for AI and product thinking
Python for data work, NumPy, Pandas, AI product fundamentals and lifecycle.
- 2
LLM APIs and prompt engineering
OpenAI and Anthropic APIs, structured outputs, few-shot, chain-of-thought, context management.
- 3
Embeddings and vector databases
Embeddings, Pinecone/Chroma, semantic search, hybrid search, quality metrics.
- 4
RAG pipelines
Document loading, chunking, retrieval, answer-quality optimisation, monitoring.
- 5
AI agents, evaluation, safety
Tool calling, multi-step agents, evaluation, hallucination control, safety.
- 6
Final AI product and deployment
Final AI application, API, demo UI, deployment, project defense.
Final project
An AI assistant or RAG application with document upload, semantic search, answer generation, API and demo UI. A full-fledged portfolio case for interviews.
A deployed AI application, a portfolio of practical cases and preparation for a Junior AI / LLM Engineer role.
Hard skills
- Python
- OpenAI API
- Anthropic API
- LangChain
- RAG
- Vector DB
- Embeddings
- Pinecone
- LLM Eval
Career outcomes
- Junior AI Engineer / LLM Engineer
- Prompt Engineer in a product team
- AI Developer at startups and BigTech
Progress assessment
- 40%
Homework assignments
- 20%
Midterm checkpoints
- 10%
Participation and attendance
- 30%
Final project
FAQ
How is an AI Engineer different from a Data Scientist?
A Data Scientist usually focuses on data analysis and building models from scratch. An AI Engineer integrates ready-made LLMs and ships working AI products: pipelines, search, agents, deployment.Do I need math and ML knowledge?
Deep ML expertise is not required. You need basic Python, comfort with API documentation and willingness to experiment.Which LLMs do you use?
We work with current APIs: OpenAI, Anthropic Claude, and open-weights models via API providers. The focus is on engineering practices, not a specific model.Can I study via Tech Orda?
Studying via Tech Orda depends on the program's official rules, selection results and available quotas.
Apply to the course
Learning outcome: A deployed AI application, a portfolio of practical cases and preparation for a Junior AI / LLM Engineer role.