AI-Native Engineering
AIDLC – AI-Native Software Development Lifecycle
From AI User to AI-Native Delivery Leader
3 Progressive Levels
Capstone + Career Readiness
3 Months · 12 Weekends
12 Weeks
Why It Matters
AI is becoming embedded across Discovery, Requirements, Architecture, Development, Testing, Deployment and Operations.
Modern engineers need to move beyond occasionally asking AI questions — they need to structure context, delegate work to agents, maintain human checkpoints, and govern AI-assisted execution.
AIDLC is not a collection of AI tool tutorials or a prompting-only course. It is a framework for systematic, AI-native delivery built on established engineering discipline.
Who Should Attend
Software Engineers
Senior Engineers & Architects
QA & Automation Engineers
DevOps & SRE Professionals
Technical Managers & Engineering Leads
Product & Program Managers
Solution Engineers
Fresh Graduates with a technical interest
Learning Journey
LEVEL 1 · WEEKENDS 1–3
AI Enabler
AI Foundations · Prompt Engineering · Context + Harness Engineering · Agents + Skills
Outcome: Use AI Effectively
→
LEVEL 2 · WEEKENDS 4–9
AI Accelerator
Product · Architecture · Development · Quality · DevOps · End-to-End AIDLC
Outcome: Execute Delivery With AI
→
LEVEL 3 · WEEKENDS 10–12
AIDLC Expert
Solution Engineering · Technical Leadership · Governance · AI Orchestration · Capstone Defense
Outcome: Orchestrate & Lead
Curriculum
Level 1 — AI Enabler
Level 2 — AI Accelerator
Level 3 — AIDLC Expert
What You’ll Achieve
Use AI systematically and reliably across the engineering lifecycle.
Execute product, architecture, development, quality and DevOps work with AI assistance.
Orchestrate, govern and lead AI-native software delivery.
Present and defend a complete end-to-end capstone solution.
Technology Ecosystem
AI / LLM tooling
Context & RAG workflows
AI Agents & reusable Skills
Java, Python, Node.js, .NET (context-guided)
Cloud-native deployment (AWS examples)
CI/CD & observability tooling
Capstone / Portfolio
Kick-off in Weekend 9, built and refined across Weekends 10–11, presented and defended in Weekend 12.
Evaluated across Product, Architecture, Development, Quality, Deployment & Operations, AI Orchestration, Governance and Executive Communication.
Artifacts can include requirements, product spec, architecture & ADRs, implementation, test strategy, deployment strategy, AI agents/skills, and a final executive presentation.
Career Relevance
Career Readiness Track begins after Weekend 9, running alongside Expert training and Capstone work.
Covers role identification, resume and LinkedIn positioning, GitHub/portfolio guidance, and mock interviews.
LearnZone.ai provides Job Assistance and Career Transition Support. Placement, interviews, salary outcomes and employment are not guaranteed. Outcomes depend on individual experience, preparation, effort, market conditions and employer decisions.
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