AI Engineer

Python is the language more people learn to code with than any other, and for good reason. It reads like plain English, runs everywhere, and has a library for virtually anything you want to build. From AI to web development to scripting, Python is the starting point for most of what matters in software today.

• Machine learning models and AI applications
• Data analysis and scientific computing
• Web backends with Django and FastAPI

TypeScript is JavaScript with a type system. Large JavaScript codebases are notoriously hard to maintain because errors only show up at runtime. TypeScript catches those problems before you run a single line of code, and at scale it makes a dramatic difference.

• Large-scale frontend applications in React or Vue
• Node.js and Nest.js backends with type-safe APIs
• Shared type definitions across frontend and backend

Docker solved the 'it works on my machine' problem. You package your application and everything it needs into a container that runs identically on a developer laptop, a test server, and production. Consistent environments, no surprises.

• Packaging applications so they run the same everywhere
• Running services locally without installing them natively
• Building CI/CD pipelines with repeatable environments

Kubernetes orchestrates containers at scale. When you go from one Docker container to hundreds across multiple servers, you need something to manage them, roll out updates, restart failures, balance load, and scale under pressure.

• Deploying containerised applications across a cluster
• Scaling services up and down automatically under load
• Rolling out new versions with zero downtime

RAG is how you make an AI system that knows things a model was never trained on. You take your own documents, split them into chunks, convert those chunks into vector embeddings, store them in a vector database, and at query time retrieve the most relevant chunks and pass them to the model as context. This is the technique behind every serious AI application that needs to work with private or recent data.

• Vector embeddings and how semantic search works
• Choosing and configuring a vector database
• Chunking strategies and their effect on retrieval quality