Enroll in AI/ML Track
Applied Engineering Curriculum

Production-grade AI/ML Systems

Master transformer architectures, fine-tuning, and low-latency retrieval pipelines using Python, PyTorch, LangChain, and Qdrant in a rigorous cohort environment.

12 wks

cohort duration

PyTorch

runtime stack

Qdrant

vector engine

Core Modules

Applied Machine Learning Focus Areas

Rigorous technical tracks designed to bridge computer science theory and production deployment.

Embeddings & Vector Search

Agentic Tool Use

Evaluation Frameworks

Optimize latency and retrieval precision with custom vector indexing, semantic caching, and sparse-dense hybrid retrieval pipelines.

Engineer autonomous agent loops, function-calling validation layers, and multi-step reasoning frameworks robust against execution drift.

Deploy rigorous model evaluation suites, hallucination mitigation protocols, and systematic cost-latency optimization benchmarks.

The Engineering Standard

Prompt tutorials fail at scale. Robust system architecture endures.

We engineer reliable context pipelines and production-grade agentic systems for engineers building real-world software.

Enroll in AI/ML Track

Secure your place in the upcoming AI/ML cohort

Applications are reviewed on a rolling basis. Technical prerequisites and coding interview apply.