AI Precision Medicine Lead

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

SQL is the language databases speak. You use it to retrieve exactly what you need from large stores of structured data, shape that data into something useful, and define the tables that hold everything together. Almost every application that stores information uses SQL under the hood.

• Querying and filtering data in relational databases
• Building backend data layers for web and mobile apps
• Reporting pipelines and business intelligence dashboards

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

The most advanced open-source relational database in the world. Handles complex SQL queries, stores rich data types, enforces data integrity, and scales to serious production workloads without a licensing fee.

• Storing relational data for web and mobile applications
• Complex queries with joins, aggregations, and window functions
• Storing JSON and running hybrid relational queries on it

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