Data and AI

AI Engineering Portfolio

Build a complete, genuine end-to-end ML/AI project demonstrating the full skill stack from this course sequence, and prepare for real ML interviews

12h4 lessonsTier 2 hireabilityTier 2 certificate

What you'll be able to do

You have a complete, deployed end-to-end ML/AI portfolio project demonstrating real technical depth, and can confidently approach ML system design and coding interviews with a structured, practiced approach.

Job titles this qualifies you for

Machine Learning EngineerAI EngineerLLM EngineerMLOps Engineer
Market intelligence
Demand
High
Remote
70% remote roles
Nigeria salary
Not a primary local hiring category
Remote (USD)
$101,500–$155,000/year for the typical remote range (25th-75th percentile); averages reported between $128,769 and $195,475/year depending on source; specialised skills (AI agent architecture, LLM fine-tuning) can increase pay up to 25%

Lessons

Stage 1 — Foundation
1
Designing a Genuinely Differentiated AI Portfolio Project
30 min
2
Presenting Technical ML Work to Mixed Audiences
25 min
Stage 2 — Applied
1
ML System Design Interview Practice
40 min
2
Coding Interview Practice and Realistic Job Search Expectations
25 min
Tier 2 Proof Submission

AI Engineering Portfolio Capstone

Demonstrate a genuine, differentiated end-to-end AI/ML project, honest technical communication, ML system design structure, and interview/job-search readiness.

1.A described end-to-end project idea with documented judgement calls
2.A README opening and one honest documented limitation
3.A structured ML system design answer with clarifying questions
4.A skill-prioritisation explanation for a specific job posting
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