What is an AI Role?
Let's start with stories and real-life analogies. No jargon — just pure understanding.
Imagine a Robot Restaurant
You walk into a fancy restaurant where everything is run by robots and computers. To make this restaurant work, you need many different kinds of helpers — not just one robot!
AI systems are like restaurants — they need a whole team of specialists, not just one person. Each AI role is like a different job in the kitchen!
Visual Explanation
Click each role in the AI pipeline to explore how they connect. See what each person does step-by-step.
The AI Production Pipeline
Click any node to learn more about that role ↓
Engineer
Scientist
Researcher
Engineer
PM
Ethicist
Live!
All 6 Roles — Click to Expand
Skills Each Role Needs Most
Interactive Playground
Drag tasks to roles, match responsibilities, and experiment freely!
🎯 Task Matching Challenge
Drag each task card to the correct role bucket below. Score points for correct matches!
💰 Salary Explorer
Use the slider to see how experience affects salaries (US market, USD thousands)
🏢 Work Environment Comparison
Toggle company type to see how roles change
Guided Deep Dive
Now let's go deeper. Real job descriptions, required skills, day-in-the-life, and how roles interconnect.
📋 Real Job Description
Analyse large datasets
Find patterns and insights using statistical methods
Build predictive models
Machine learning models that forecast outcomes
Communicate findings
Translate complex results into business decisions
A/B Testing
Design and analyse experiments
Feature engineering
Create meaningful inputs for ML models
🛠 Required Skills
⏰ Day in the Life
9:00 AM — Data Exploration
Load dataset, check distributions, identify anomalies, visualise features
11:00 AM — Model Training
Try 3 different algorithms, compare validation scores, tune hyperparameters
2:00 PM — Stakeholder Meeting
Present findings to product team, discuss business impact
4:00 PM — Documentation & Code Review
Write up findings, peer review a teammate's notebook
🤔 Common Misconceptions
▼❌ Myth: Data Scientists only do machine learning.
✅ Reality: 80% of the job is data cleaning, exploration, and communication. ML is one tool among many.
❌ Myth: You need a PhD to be a Data Scientist.
✅ Reality: Many DS professionals have bachelor's or master's degrees. Portfolio projects matter more than degrees.
📋 Real Job Description
Train & deploy ML models
Move models from research to production systems
Build ML infrastructure
Pipelines, feature stores, model registries
Performance optimization
Reduce latency, increase throughput, cut costs
Monitoring & retraining
Detect model drift, automate retraining
Scale systems
Handle millions of inference requests
🛠 Required Skills
⏰ Day in the Life
9:00 AM — System Monitoring
Check model performance dashboards, investigate any drift alerts
10:30 AM — Feature Pipeline
Build new feature transformation for production recommendation model
1:00 PM — Deployment
Deploy updated model to staging, run integration tests, canary rollout
3:30 PM — Architecture Review
Design new vector database setup for semantic search
📋 Real Job Description
Design data pipelines
ETL/ELT systems that move data from source to destination
Build data warehouses
Structured storage optimized for analytics
Ensure data quality
Validation, testing, monitoring data health
Stream processing
Real-time data with Kafka, Spark Streaming
Infrastructure as Code
Cloud infrastructure automation
🛠 Required Skills
📋 Real Job Description
Design experiments
Formulate hypotheses about new AI approaches
Publish research papers
Share findings with the scientific community
Develop new architectures
Create novel neural network designs
Benchmark models
Compare against state-of-the-art on standard tasks
Collaborate with engineers
Help bridge research and product teams
🛠 Required Skills
📋 Real Job Description
Define AI product vision
Set direction for what to build and why
Gather user requirements
Translate user needs to technical specifications
Prioritize roadmap
Decide what to build first, balance trade-offs
Coordinate across teams
Align DS, MLE, DE, design, and business
Measure success
Define and track product metrics and KPIs
🛠 Required Skills
📋 Real Job Description
Audit AI systems for bias
Test fairness across demographic groups
Develop ethical guidelines
Create principles for responsible AI use
Regulatory compliance
Ensure AI meets legal and ethical standards
Red-teaming
Actively try to find harmful failure modes
Stakeholder education
Train teams on responsible AI practices
🛠 Required Skills
Advanced Concepts
Deeper mechanics, real tools, trade-offs, and how these roles evolved over time.
🛠 Full Technology Stack
📈 Career Progression Simulator
Drag to set years of experience and see how responsibilities shift
🔀 Where Roles Overlap
Click an overlap zone to see shared responsibilities
Real-World Applications
How AI teams work at real companies. Case studies and org structures.
🏢 Case Studies — Real AI Teams
👥 Build Your AI Team
You're starting a new AI product. Choose your first 3 hires and see the trade-offs!
Experiment Zone
Free play! Simulate an AI project lifecycle and see what breaks when you remove key roles.
🎮 AI Project Simulator
Toggle team members ON/OFF and hit "Run Project" to see the consequences!
💥 "Try Breaking It" Scenarios
What happens when something goes wrong? Click a scenario to see the impact.
Test Your Knowledge
Questions range from beginner to expert. Get instant feedback on every answer!
Quiz Complete!
Master-Level Challenges
Apply your knowledge. These require deep thinking — no multiple choice here!
Team Composition Design
A startup has just raised $2M seed round. They want to build an AI-powered customer support chatbot. They can only afford 2 people.
The Broken Pipeline
A large e-commerce company's recommendation system suddenly starts recommending only men's products to all users, regardless of gender. The model was working fine last month.
The Org Design Problem
You're the Head of AI at a 500-person company. The CEO wants to integrate AI into every department. You have budget for 20 AI hires across 5 product lines, plus a central AI platform team.
You've Mastered AI Roles!
Here's your complete reference sheet — everything condensed into key insights.
📋 The Ultimate Cheatsheet
🧠 Key Mental Models
You're now an AI Roles Expert!
Share your knowledge and keep learning — the AI field evolves daily.