Don’t just use AI. Build useful systems with it.
What you’ll work on
- Build AI features such as chatbots, RAG pipelines, and intelligent automation for client products
- Work with LLM APIs, prompting, evaluation, and cost considerations
- Integrate AI services into real web and mobile applications
- Investigate how AI systems behave, where they fail, and how they can be improved
- Work with guardrails, evaluation, and monitoring when deploying AI functionality
Professional capabilities you’ll develop
- Turning a product requirement into an appropriate AI solution
- Working with LLM APIs and prompts
- Designing and understanding RAG systems
- Evaluating AI outputs instead of blindly trusting them
- Thinking about reliability, cost, security, and failure cases
- Integrating AI into existing applications
- Understanding the difference between an AI demo and a production feature
Technologies you may work with
- Python
- LLM APIs
- RAG architectures
- Vector databases
- LangChain/LlamaIndex
- Model evaluation tools
- AI deployment tools
Examples of real work
- Customer support assistants using client knowledge bases
- Document intelligence and extraction pipelines
- AI-powered features inside client web and mobile applications
Who this suits
You’re curious about what AI can actually do in a product, but you also want to understand its limitations. You want to build useful systems rather than simply experiment with prompts.
Prerequisites
Prerequisites are a starting point, not a promise that you already know everything. You're expected to learn during the experience — but you also need enough foundation to participate in real work and take responsibility for your own progress.
AI Engineer
- Basic programming fundamentals
- Python fundamentals
- Basic understanding of APIs
- Basic Git knowledge
- Curiosity about how AI systems work
- Willingness to understand and evaluate AI-generated output rather than blindly rely on it
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