AI Team
A dream team setup that unleashes talent to build AI products
🏛️ Before LLMs
Before LLM APIs, AI teams focused on specialists for model development from scratch.
AI Product Manager
Defines product vision and metrics
Aligns AI with business goals
Data Scientist
Designs AI/ML models
Runs experiments and analyses data
Builds and tunes models
Maintains model performance
ML Engineer
Bridges data science and production
Scales models in production
Integrates models into codebases
Data Engineer
Sets up data pipelines
Ensures clean data for training
Provides scalable infrastructure
Lays the foundation for data-driven AI
Software Engineer (normally sits outside the model team)
Integrates AI with frontend and backend
Connects data science to user experience
Delivers speed and functionality
🚀 With LLMs
With LLM APIs, teams evolve towards integration-focused workflows while maintaining core expertise.
AI Product Manager
Defines vision and metrics
Aligns capabilities with business goals
Software Engineer
Builds UI and backend logic
Integrates AI features
Handles data flow between UI, backend, and APIs
Maintains product performance
ML Engineer
Evaluates quality of LLM outputs
Ensures alignment with goals
Identifies bugs and performance issues
Tests for accuracy and bias
AI Engineer
Builds full-stack AI applications
Uses LLMs to solve real problems
Applies LLMs to products
Measures AI performance
Performs prompt engineering
Data Scientist (evolved role)
Adapts expertise to LLM evaluation and optimisation
Analyses LLM performance patterns
Designs experiments for prompt effectiveness
Maintains statistical rigour in AI assessments
⚡ Advanced Teams
For fine-tuning and advanced features, specialist roles expand their responsibilities.
Data Scientist (expanded scope)
Fine-tunes models with company data
Evaluates LLM performance
Runs experiments with different models
Identifies and fixes biases
Bridges traditional ML with LLM capabilities
Data Engineer (enhanced role)
Manages pipelines for fine-tuning
Ensures data quality
Maintains data lakes and warehouses
Supports real-time data via prompts
Adapts infrastructure for LLM workflows
ML Engineer (evolved responsibilities)
Integrates LLM API calls into production
Ensures scalable API integration
Sets up MLOps for LLM integration
Monitors API performance
Combines traditional MLOps with LLM operations
AI Engineer
The current job market (2025) shows AI engineer positions require hybrid skill sets, combining traditional ML expertise with LLM integration capabilities. Organisations value both foundational AI/ML engineering skills and modern LLM application abilities.
🧱 Scaling Infra
Essential capabilities for scaling AI.
LLM Accessibility
Provides easy LLM access
Standardises integration
Reduces adoption barriers
Observability Platform
Monitors AI performance
Tracks usage and costs
Identifies optimisation opportunities
Evaluation Support
Measures AI effectiveness
Provides testing frameworks
Ensures quality standards
Feedback Loop
Captures user interactions
Enables continuous improvement
Informs feature development
Experimentation Sandbox
Provides a safe testing environment
Enables rapid prototyping
Allows risk-free exploration
Recipes and Demos
Offers implementation examples
Accelerates development
Models best practices
🪄 Transitioning
💡 What types of leaders do you need to transition from a traditional team to an AI team?
Operator — focused on maintenance and functioning, through stability and security.
Guide — focused on humanistic growth and supporting others, through affiliation and bonding.
Achiever — focused on victory and success, through sheer drive and achievement.
Catalyst — focused on transformation and innovation, through inspiration and engagement.
💡 Leaders need to cover all four areas, but which ones should they focus on?
Tech Lead EM: tech-led, people + delivery
Team Lead EM: people-led, tech + delivery
Delivery EM: delivery-led, tech + people
Product EM: product-led, tech + delivery. (I think this can be a technical product manager)
Lead of Leads EM: although they are team and delivery focused, you need to be one of 1-4 before you can be a lead of leads.
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