AI Engineering Apprenticeship Program

Program Overview

Mission

Develop engineers who can leverage AI tools effectively while maintaining deep technical understanding and professional engineering standards.

Core Philosophy

• Real-world standards from day one
• AI as a tool, not a crutch
• Understanding > Output
• Professional accountability for all code

Duration

• 6 months intensive
• 20-30 hours/week commitment
• Remote-first with synchronous reviews

Curriculum Structure

Module 1: Foundations & Algorithmic Thinking (4 weeks)

Project: Unbeatable Tic-Tac-Toe Engine

Technical Goals:

• Environment setup (Git, Python, testing frameworks)
• Algorithm design and implementation (minimax, alpha-beta pruning)
• Performance optimization
• Comprehensive testing

Professional Skills:

• Code organization and documentation
• PR-based workflow
• Code review participation
• Performance profiling

AI Integration:

• Use AI for learning concepts
• Generate test cases
• Explore optimizations
• Must defend all implementation choices

Deliverables:

• Working game with unbeatable AI
• Comprehensive test suite
• Performance benchmarks
• Technical documentation

Module 2: Systems Programming (4 weeks)

Project: HTTP Server from Scratch in Rust

Technical Goals:

• TCP/IP socket programming
• HTTP protocol implementation
• Concurrent request handling

Professional Skills:

• RFC interpretation
• Error handling strategies
• System debugging
• Resource management

AI Integration:

• Protocol clarification
• Debugging assistance
• Pattern exploration
• Performance optimization ideas

Deliverables:

• HTTP/1.1 compliant server
• Benchmark results
• Architecture documentation
• Comparison with production servers

Module 3: Language Processing & Type Systems (4 weeks)

Project: JSON Parser with Query Language

Technical Goals:

• Parser combinators
• Type-driven development
• Error handling and reporting
• Query language design

Professional Skills:

• API design
• Documentation-first development
• Property-based testing
• Type system leverage

AI Integration:

• Grammar exploration
• Test case generation
• Type signature assistance
• Edge case discovery

Deliverables:

• Full JSON parser
• Query language implementation
• Comprehensive type safety
• Usage documentation

Module 4: Distributed Systems (4 weeks)

Project: Distributed Key-Value Store

Technical Goals:

• Consensus algorithm (simplified Raft)
• Network partition handling
• Consistency guarantees
• Failure recovery

Professional Skills:

• Distributed debugging
• System observability
• Chaos engineering basics
• Production readiness

AI Integration:

• Failure scenario generation
• Test harness development
• Performance analysis
• Bug reproduction

Deliverables:

• Working distributed KV store
• Chaos testing results
• Operations runbook
• Architecture decisions record

Module 5: AI Engineering (4 weeks)

Project: Domain-Specific AI Development Assistant

Technical Goals:

• Agent architecture design
• Prompt engineering
• Evaluation framework
• Tool integration
• RAG implementation

Professional Skills:

• Eval-driven development
• Cost optimization
• Safety considerations
• User experience design

AI Integration:

• Meta-level: using AI to build AI
• Evaluation automation
• Performance monitoring
• Iterative improvement

Deliverables:

• Working AI assistant
• Evaluation suite with metrics
• Cost analysis
• Improvement roadmap

Capstone Project (4 weeks)

Project: Student's Choice - Full System

Requirements:

• Must integrate multiple paradigms
• Production-ready implementation
• Comprehensive documentation
• Performance benchmarks
• AI-assisted development throughout

Program Structure

Weekly Cadence

• Monday: Module kickoff, new concepts
• Wednesday: Mid-week check-in, pairing session
• Friday: Code review, technical discussion
• Async: Project work, peer reviews

Code Review Process

1. Self-Review Checklist

   • Does it work correctly?
   • Can I explain every decision?
   • Is it tested thoroughly?
   • Would I deploy this?

2. Peer Review

   • One apprentice reviews another's code
   • Focus on understanding and clarity
   • Required before mentor review

3. Mentor Review

   • Technical correctness
   • Professional standards
   • Architecture decisions
   • AI usage effectiveness

4. Review Defense

   • Verbal explanation of key decisions
   • Live debugging demonstration
   • Performance analysis
   • Trade-off discussions

Assessment Criteria

Technical Competence

• Correctness of implementation
• Performance characteristics
• Error handling
• Test coverage

Professional Skills

• Code clarity and documentation
• Review participation
• Communication ability
• Debugging proficiency

AI Integration

• Effective use without dependency
• Understanding of generated code
• Critical evaluation skills
• Tool selection judgment

Support Structure

Mentorship

• 1:1 weekly meetings
• Code review participation
• Architecture discussions
• Career guidance

Peer Learning

• Pair programming sessions
• Group debugging workshops
• Architecture review meetings
• Knowledge sharing presentations

Resources

• Curated learning materials
• AI tool access
• Development environment
• Production-like infrastructure

Outcomes

Graduate Profile

An apprentice graduate will be able to:

• Build complex systems from first principles
• Effectively leverage AI without dependency
• Debug and maintain any codebase
• Make sound architectural decisions
• Communicate technical concepts clearly
• Work at professional engineering standards

Portfolio

Graduates will have:

• 5 non-trivial projects showcasing different paradigms
• Public code repository with professional standards
• Technical blog posts explaining their projects
• Demonstrated AI integration skills
• Real code review experience

Logistics

Prerequisites

• Basic programming experience (any language)
• Commitment to 20-30 hours/week
• Willingness to be challenged
• Growth mindset