Pick the engineer you want to become.

Build products on top of foundation models.

You want to build with large language models, not train them.

Not model training or research — that is the ML Engineer track.

• A prompt workbench with structured outputs
• Semantic search over a document collection
• A production RAG assistant with citations

Train, serve, and monitor models in production.

You want to own models end to end, from raw dataset to live endpoint.

Not calling somebody else’s API — here you build and own the model.

• An end-to-end tabular model with an honest evaluation
• An image classifier fine-tuned from a pretrained backbone
• A reproducible training pipeline with tracked experiments

Move and model data at scale.

You want to build the pipelines every other team depends on.

Not dashboards and analysis — you build the systems those run on.

• An analytics layer over a raw dataset
• An incremental ingestion job with tests
• A batch ELT pipeline: orchestration, transformation, warehouse