Phase 1: AI Engineering Foundations
Python refresh, APIs, model basics, prompt patterns, data handling, and engineering workflows.
Accelerated Career Training
Become an AI Engineer with ACT.
A 16-week guided transformation pathway for IT engineers ready to build AI applications, RAG systems, agentic workflows, and a credible career portfolio.
Who ACT is for
ACT is designed for experienced IT engineers, software developers, cloud engineers, data engineers, QA automation engineers, DevOps engineers, and technical leads who want a practical bridge into AI engineering.
- You already understand engineering workflows and want to apply them to AI systems.
- You need structured practice, not scattered tutorials.
- You want portfolio projects that demonstrate production judgment.
- You want interview readiness for applied AI engineering roles.
Curriculum arc
Phase 2: LLM Applications
Build model-powered applications with structured outputs, reliability patterns, and deployment flow.
Phase 3: RAG and Agents
Design retrieval pipelines, vector search, knowledge systems, tool-using agents, and guardrails.
Phase 4: Portfolio and Career Launch
Capstone refinement, architecture writing, interview practice, demo day, and career positioning.
Portfolio outcomes
- An LLM application with structured output and evaluation checks.
- A RAG system using embeddings, vector search, and grounded generation.
- An agentic workflow with tool use, guardrails, and human review points.
- A capstone project with architecture notes, demo material, and interview talking points.
Learning model
Built for working engineers.
ACT balances technical depth with a realistic schedule, hands-on labs, mentor checkpoints, and career deliverables.
Hands-on labs
Every module asks learners to build, test, and explain real AI engineering workflows.
Mentor review
Technical feedback focuses on architecture, evaluation, reliability, and portfolio clarity.
Career readiness
Learners translate their projects into resumes, GitHub narratives, interviews, and demos.