AI Research Scientist

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

Data structures and algorithms are the vocabulary of software engineering. You will learn how arrays, linked lists, trees, graphs, hash maps, and heaps actually work, when to use each one, and how to analyse the performance of your choices. These concepts underpin every technical interview and every piece of code that needs to perform well at scale.

• Time and space complexity analysis with Big O notation
• Core data structures: lists, stacks, queues, trees, graphs, hash maps
• Sorting and searching algorithms

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

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

Validates the ability to design, implement, and maintain machine learning solutions on AWS. Covers data engineering, exploratory data analysis, modelling with SageMaker, and deployment at scale. For ML engineers and data scientists building production AI systems on AWS.

• Data ingestion and transformation pipelines for ML
• Training, tuning, and evaluating models with SageMaker
• Deploying models as real-time and batch endpoints