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Expert Practitioner Programme

AI in
Software Engineering

Become an AI-native engineer. 8 live classes covering the full development lifecycle — prompting, code review, security, testing, and shipping production-ready work with confidence.

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What AI-Native Software Engineering Looks Like

When a developer generates production-quality code with structured prompts, conducts AI-powered code review in their CI/CD pipeline, debugs with hypothesis-first AI workflows, and ships faster with full ownership, that is an AI-native engineer.

8
Live Classes
Real
Capstone Project
Field
Verified Certificate
Programme Duration
4 Weeks
Learning Format
Live, Online
Validate Your Skill Level

Where Are You on the
AI Readiness Spectrum in Software Engineering?

5 questions. 90 seconds.

Daily Practice Question 1 of 5

How do you currently use AI tools in your day-to-day software development work?

I don't use AI tools in my development workflow
I use AI occasionally — mostly for code completion or quick Google alternatives
I use AI regularly for code generation, debugging, and documentation
AI is embedded in my full SDLC — from planning through to review and deployment
Prompting Skill Question 2 of 5

When you ask an AI model to generate or fix code, how would you describe the quality of your prompts?

I type whatever comes to mind and hope for a useful response
I include some context but don't have a consistent structure
I structure prompts with role, context, constraints, and expected output format
I maintain a personal prompt library and use chain-of-thought and few-shot techniques
Code Quality & Review Question 3 of 5

How confident are you reviewing AI-generated code before committing it to production?

Not confident — I copy-paste and hope it works
I spot obvious bugs but miss security issues and hidden coupling
I review AI output systematically — checking for security, readability, and edge cases
I have team-wide review standards for AI-generated code with documented criteria
Security Awareness Question 4 of 5

How aware are you of the specific security risks introduced by AI-generated code?

I haven't thought about this specifically
I know it's a risk but don't have a structured approach to catch issues
I check for common vulnerabilities — injection, insecure defaults, exposed secrets
I lead security reviews for AI-generated code and set safe AI usage rules for my team
Applied Impact Question 5 of 5

What is the most significant thing you have shipped using AI assistance?

I haven't shipped anything using AI yet
I've used AI to write tests, documentation, or small utility functions
I've shipped a feature or API with significant AI-assisted code that is live in production
I've built repeatable AI workflows that my team uses and that measurably improve velocity
—

Programme Curriculum

8 Classes. Full AI Engineering Lifecycle.

Each class builds on the last — from model landscape and prompting fundamentals through to a live capstone defence and your 30-day post-programme roadmap.

Content · 8 classes
Class 1Foundations & Environment Setup
  • What "AI-native" means vs AI-assisted vs AI-aware development
  • Deep-dive comparison of major coding models: Claude, GPT-4o, Gemini, DeepSeek, Qwen, Mistral, LLaMA, StarCoder2
  • IDE setup: VS Code, JetBrains, GitHub Copilot, Cursor, and local models via Ollama
  • Anatomy of a strong engineering prompt — role, context, task, constraints, output format
Class 2Practical Interactions, Documentation & Planning
  • Advanced prompt engineering — multi-step, iterative, and chained prompts
  • AI for requirements: turning vague briefs into technical specs, user stories, and architecture decisions
  • Technical documentation at scale: README, OpenAPI/Swagger, ADRs, release notes, onboarding docs
  • Understanding legacy codebases with AI — explaining mystery functions and mapping dependencies
Class 3Code Generation, Review & Refactoring
  • Generating production-ready functions, APIs, and modules — and knowing what to validate before committing
  • AI-assisted refactoring — naming, modularity, design patterns, and breaking monoliths
  • Using AI as a structured first-pass code reviewer: readability, performance, security, design quality
  • The right mental model: what to delegate to AI vs what must remain with the engineer
Class 4Debugging, Testing & Quality Assurance
  • Feeding stack traces and logs to AI effectively — hypothesis-first debugging workflows
  • AI-driven test generation: unit, integration, and E2E — with edge cases AI commonly misses
  • TDD workflows assisted by AI, and reviewing AI-written tests for shallow or false coverage
  • Performance profiling and static analysis with AI-guided interpretation
Class 5Security, Ethics & Safe AI Practice
  • Common vulnerabilities in AI-generated code: injection, insecure defaults, hardcoded credentials
  • What not to put in prompts: PII, credentials, client data, proprietary logic — and when to use local models
  • Hallucination: why it happens, how to detect it, and how to verify AI outputs
  • Building a personal AI safety policy — licensing, data retention, and professional accountability
Class 6AI-Enhanced Productivity & Workflows
  • Mapping AI tools to each SDLC phase — requirements, design, build, test, deploy
  • Building reusable prompt templates and a personal AI workflow OS
  • Standardising AI usage across an engineering team — shared libraries and norms
  • Full productivity lab: take a real feature from brief to working code, tracking AI prompts throughout
Class 7Capstone Project & Defence
  • Build a real project: working code on GitHub with prompt log, test suite, and reflection document
  • Project tracks: beginner (CRUD app / API), intermediate (authenticated REST / AI feature), advanced (multi-service / CI-CD pipeline)
  • Live 5–10 minute defence: walk through your AI workflow, justify two decisions where you overruled the AI
  • Practitioner and peer Q&A — rigorous, practical, and employer-ready
Class 8What's Next — The Future of AI in Engineering
  • Honest assessment of where AI coding tools are today — capabilities, limits, and what's overhyped
  • Agentic AI: autonomous coding agents (Devin, SWE-agent, Claude Code, OpenHands) — what they can and cannot do
  • Reasoning models vs generation models, longer context windows, multimodal coding
  • Career positioning: how to talk about AI fluency, skills that stay irreplaceable, your 30-day implementation roadmap
Tools & Platforms

15+ AI Tools Across the Full Development Stack

You will work hands-on with the tools that AI-native engineers actually use — from AI-powered IDEs to local model inference. Real stacks, not toy environments.

Claude (Anthropic)
GPT-4o / Codex
GitHub Copilot
Cursor
Windsurf
VS Code + AI Extensions
Gemini Pro / 2.0
DeepSeek Coder
Ollama (local models)
LM Studio
Aider (terminal AI)
GitHub Actions (AI)
Claude Code
CodeRabbit
Qwen2.5-Coder
Programme Cohorts

Open Cohorts

Cohort-based delivery means you learn alongside peers in your field and leave with a network, not just a certificate.

Who Is This For

Built for Practising Software Engineers

This programme is for engineers who are already writing code professionally and want to operate at the level AI makes possible — systematically, securely, and fast.

Backend Developer
Frontend / Full-Stack Dev
DevOps / Platform Engineer
Mobile Developer
Engineering Tech Lead
QA / Test Engineer
Eligibility Criteria
  • ✓Software engineer, developer, or technical lead with 1+ years of professional coding experience
  • ✓Comfortable writing and reading code in at least one programming language
  • ✓Readiness to build a real capstone project and defend it live
Apply Now
The Sprint

How the Engineering & Infrastructure
Sprint Works

🎯
Week 0

Field Assessment

Map your current AI maturity and identify the highest-leverage skills for your specific role in Engineering & Infrastructure.

👥
Week 1

Placed in Your Cohort

You join a small, curated cohort of Engineering & Infrastructure professionals — led by a verified Expert Practitioner who has deployed AI in your exact domain.

🚀
Weeks 2–4

Structured Sprint

Live sessions, applied projects, and peer accountability. Every session builds directly on your Engineering & Infrastructure context — not generic AI theory.

🏆
End of Sprint

Certification

Receive your Brixgate Expert Practitioner Certificate — field-verified, practitioner-assessed, and employer-ready in Engineering & Infrastructure.

Your Practitioner

Taught by Someone Who Has Done It

Not a generalist trainer. A practitioner who has deployed AI in this exact field and will show you exactly how.

Francis Ayomide Adedeji
Francis Ayomide Adedeji
AI Engineer & Educator, AI-Native Software Engineer
20+ live sessions

Francis Ayomide Adedeji is an AI Engineer with years of experience designing, deploying, and teaching AI systems across education, fintech, and enterprise environments. He specializes in agentic AI workflows, RAG architectures, and developer enablement, building tools that bridge the gap between AI research and production engineering.

Core Expertise
Agentic AI RAG Systems Python n8n LangChain
Alumni Reviews

What Practitioners Say

★★★★★

"I used to spend hours writing boilerplate and hunting down obscure bugs. After Class 3, I have a structured review process for every piece of AI-generated code I ship. My PRs are tighter and my code reviews are faster."

Backend Developer · Brixgate Alumni
★★★★★

"The security class changed how I look at AI-generated code entirely. I found three injection vulnerabilities in our codebase within a week of applying the review checklist. We had shipped that code six months ago."

Full-Stack Developer · Brixgate Alumni
★★★★★

"I finished the capstone with a working REST API, a full test suite I actually understand, and a prompt log that shows exactly how I built it. That artefact alone got me noticed in my next job interview."

Software Engineer · Brixgate Alumni
FAQ

Common Questions

Do I need to be a senior engineer?+

No — but you do need to be actively working in software engineering. This is not a beginner coding course. You need 1+ years of professional experience writing code. The capstone track you choose (beginner, intermediate, advanced) will match your level.

Which programming languages are covered?+

The programme is language-agnostic. The prompting, review, security, and workflow principles apply regardless of your stack. Practitioner examples and live sessions use Python, JavaScript/TypeScript, and Go — but your capstone can be in any language.

Is AI-generated code actually safe to ship?+

Yes — with proper review. The programme dedicates an entire class to security and responsible AI use. You will leave with a concrete review checklist and personal AI safety policy designed for production environments.

Can I build my capstone project on real work from my job?+

Yes — and we encourage it. The capstone is designed to be a real project you deploy immediately. Just ensure you are not committing proprietary code to a public repository, and follow your employer's AI usage policy (we cover this in Class 5).

Why This Investment Pays Off

What ₦250,000 Actually Buys You

AI-native engineers ship faster, review smarter, and command higher salaries. This programme gives you the workflows, judgment, and certificate to prove it.

55%
of developers using AI tools report measurable gains in personal productivity
GitHub Developer Survey 2024
3×
Faster code delivery for teams with structured AI-assisted development workflows
McKinsey Global Institute
$45K+
Salary premium for AI-fluent senior developers over AI-unaware peers
Stack Overflow / Hired.com
What You Get

Everything Included. No Extras.

✓
8 Live Practitioner Sessions
2 sessions per week across 4 weeks — interactive, applied, no pre-recorded lectures.
✓
Pre-Programme Diagnostic
A baseline assessment before Day 1 — so the practitioner can personalise support across all 8 classes.
✓
All AI Tools & Platform Access
Every tool used in the sprint is provided. No additional subscriptions required.
✓
Peer Cohort of Engineering & Infrastructure Professionals
Learn alongside practitioners solving the same problems you face — a network, not just a cohort.
✓
Capstone Project & Portfolio Artefact
A real AI-Augmented Engineering Workflow — built during the sprint, reviewed by your practitioner, and yours to deploy.
✓
Brixgate Expert Practitioner Certificate
Verifiable digital credential, LinkedIn badge, and field-verified assessment — employer-ready in Engineering & Infrastructure.
✓
Lifetime Alumni Network Access
Post-sprint access to the Brixgate Alumni community across all fields and cohorts.
By the End of Week 4, You Will Have
→Ship a working capstone project with full prompt log, test suite, and practitioner sign-off
→Write structured prompts that produce production-ready, secure, reviewable code
→Build and document a repeatable AI workflow your team can adopt immediately
vs. The Alternatives
YouTube / Free AI Courses
Generic. No field context. No practitioner. No accountability.
Free — but valueless for your career
LinkedIn Learning / Coursera
Self-paced. Pre-recorded. No peer cohort. No field practitioner.
$400/yr · No accountability
2-Day AI Workshop
High cost. Generic content. No follow-through. No deliverable.
₦500K–₦2M · No outcomes
Brixgate Expert Practitioner Sprint
Live. Field-specific. Practitioner-led. Real deliverable. Certified.
₦250,000 · Everything included
Apply Now
Pricing

Invest in Your AI Advantage

Engineering & Infrastructure Expert Practitioner Programme

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Join the Waitlist.

Drop your details below and we'll notify you the moment enrolment opens — plus you'll get early access before we announce publicly.

✓ First access before public launch
✓ Exclusive early-bird pricing
✓ No commitment — just your spot in the queue

Ready to Become an AI-Native
Software Engineer?

Cohort 1 opens June 22, 2026. Seats are limited. Apply in under 2 minutes — no essays, no references required.

Apply Now Validate Your Level First
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