Team & Skills Requirements
Who You Need to Make AI Work
The skills, team size, and expertise required for AI projects vary dramatically by technology tier. A Tier 0 automation might need zero AI specialists, while a Tier 5 agentic system requires a research-caliber ML team. This guide breaks down what you need at each tier—from citizen developers to PhD-level researchers.
Key principle: Don't over-hire for simple problems or under-invest in complex ones. Match your team capabilities to your tier requirements.
Minimal AI Skills Required
Who You Need
Business analysts, operations staff, or "citizen developers" who understand the process being automated. Minimal to no AI expertise required—these are rule-based systems that can be configured through low-code platforms.
Required Skills
- Business process understanding
- Basic logic/if-then thinking
- Platform configuration (e.g., Zapier, Power Automate)
- Testing and validation
- No programming or data science needed
Team Size
0.25-1 FTE: Often part-time involvement from existing business staff. One person can manage multiple Tier 0 automations.
Internal vs External
Strongly prefer internal: These are business process automations. Your own staff understand the workflows best. External consultants can help with initial setup and training, but ongoing management should be internal.
Training Existing Staff
Highly trainable: Your business analysts and operations staff can learn low-code automation tools in days to weeks. Invest in training rather than hiring. Platforms like Power Automate and Zapier have extensive learning resources.
Typical Roles
- Business Analyst: Designs automation logic
- Operations Manager: Validates and monitors
- Citizen Developer: Implements in low-code platform
Hiring expensive data scientists or ML engineers for Tier 0 work. This is overkill and wastes talent. Business analysts with low-code training are perfect for Tier 0 automation.
Basic Integration Skills
Who You Need
Software developers or integration specialists who can call APIs and integrate pre-trained AI services (like OpenAI, Google Vision, AWS Rekognition). No ML expertise required—you're consuming AI, not building it.
Required Skills
- API integration and REST/HTTP basics
- Basic programming (Python, JavaScript, or similar)
- JSON/data format handling
- Error handling and retry logic
- Understanding of rate limits and costs
- No ML knowledge required
Team Size
0.5-2 FTEs: One developer can integrate multiple AI APIs. Larger implementations may need a small team for frontend, backend, and testing.
Internal vs External
Prefer internal with external kickstart: Your developers can absolutely do this work. Consider external help for architecture design and initial integration, then hand off to internal team for maintenance and expansion.
Training Existing Staff
Very trainable: Any competent software developer can learn API integration in days. If your developers can integrate Stripe or Twilio, they can integrate OpenAI. Vendor documentation is typically excellent.
Typical Roles
- Full-Stack Developer: Integrates AI APIs into applications
- Backend Developer: Handles API calls and data processing
- Solutions Architect: Designs integration approach
Hiring "AI engineers" when you just need API integration. Tier 1 is software engineering, not machine learning. Your existing developers can do this with minimal training.
Data Science Team Required
Who You Need
Data scientists who can train, tune, and validate ML models on your data. This is where true ML expertise becomes necessary. You're not just integrating APIs—you're building custom models that learn from your specific data.
Required Skills
- Statistical modeling and ML fundamentals
- Python/R with ML libraries (scikit-learn, XGBoost, etc.)
- Data preprocessing and feature engineering
- Model evaluation and validation techniques
- Experiment tracking and versioning
- SQL and data manipulation
- Understanding of model bias and fairness
Team Size
2-5 FTEs: Minimum 1-2 data scientists for modeling, 1 data engineer for pipelines, 1 software engineer for integration. Larger projects need bigger teams.
Internal vs External
Hybrid approach works well: If you don't have data scientists, partner with external experts for initial model development. They can also train your staff. Long-term, build internal capability—you'll need to retrain and iterate.
Training Existing Staff
Possible but takes time: Analysts with strong stats/math backgrounds can upskill to data science in 6-12 months with structured training. Software engineers can also transition. Bootcamps and online programs (Coursera, Fast.ai) can help, but expect a learning curve.
Typical Roles
- Data Scientist: Builds and tunes ML models
- Data Engineer: Builds data pipelines and infrastructure
- ML Engineer (entry-level): Helps with model deployment
- Domain Expert: Validates model outputs and features
Hiring junior data scientists without senior oversight, or expecting one person to do data science, data engineering, and software development. Tier 2 needs a team with complementary skills—don't expect unicorns.
ML Engineering Team Required
Who You Need
Experienced ML engineers and data scientists with deep learning expertise. This tier involves neural networks, computer vision, NLP—technologies that require specialized knowledge and significant compute resources.
Required Skills
- Deep learning frameworks (PyTorch, TensorFlow)
- Neural network architectures (CNNs, RNNs, Transformers)
- Transfer learning and fine-tuning
- GPU/distributed computing
- MLOps and model serving at scale
- Handling unstructured data (images, text, audio)
- Model optimization and deployment
- Experience with production ML systems
Team Size
5-12 FTEs: 2-4 senior ML engineers/data scientists, 1-2 MLOps engineers, 2-3 data engineers, 1-2 software engineers for integration, plus domain experts.
Internal vs External
Partner heavily with external experts: Unless you're a tech company, you probably don't have this expertise in-house. Partner with specialized ML consultancies or vendors. Build internal capability over time, but expect to rely on external help initially.
Training Existing Staff
Difficult and time-consuming: Deep learning requires advanced skills. Your Tier 2 data scientists can upskill to Tier 3, but it takes 1-2 years of focused learning and practice. More realistic to hire experienced talent or partner externally.
Typical Roles
- Senior ML Engineer: Designs and trains deep learning models
- MLOps Engineer: Deploys and monitors models at scale
- Computer Vision / NLP Specialist: Domain-specific expertise
- Data Engineer: Handles large-scale data pipelines
- ML Infrastructure Engineer: Manages GPU clusters and training infrastructure
Underestimating the specialization required. "Data scientist" is too generic at this tier—you need people with specific deep learning experience (CV, NLP, etc.). Also, don't skip MLOps expertise; production deep learning is hard.
Advanced ML Team & Infrastructure
Who You Need
Senior ML leaders, specialized researchers, and infrastructure experts who can orchestrate multiple models, build ML platforms, and solve novel problems. This is enterprise-scale ML requiring organizational investment.
Required Skills
- Multi-model orchestration and ensemble methods
- ML platform architecture (feature stores, model registries)
- Advanced MLOps (A/B testing, canary deployments)
- Distributed systems and large-scale infrastructure
- Custom model architectures and research implementation
- ML governance, monitoring, and observability
- Cost optimization for ML at scale
- Team leadership and project management
Team Size
12-30+ FTEs: 4-8 senior ML engineers/researchers, 3-5 MLOps engineers, 3-5 data engineers, 2-3 ML infrastructure engineers, plus supporting roles (PM, DevOps, QA).
Internal vs External
Build internal capability with external acceleration: At this tier, you need strong internal leadership (Head of ML/AI). Partner with external experts for specialized components, but own the architecture and strategy internally.
Training Existing Staff
Grow from Tier 3: If you've successfully executed Tier 3 projects, you can grow your team's capabilities to Tier 4. Invest in senior hires to lead, and provide your mid-level team members with training and mentorship. External training partnerships can accelerate.
Typical Roles
- Head of ML/AI: Strategic leadership and vision
- Principal ML Engineer: Technical leadership and architecture
- ML Research Engineer: Implements cutting-edge techniques
- Senior MLOps Engineer: Production systems at scale
- ML Platform Engineer: Builds internal ML tooling
- ML Product Manager: Defines ML product strategy
Jumping to Tier 4 without Tier 2-3 experience. You can't hire your way to Tier 4 capability—your organization needs to learn how to do ML first. Also, don't neglect leadership; Tier 4 needs strong technical leadership, not just individual contributors.
Research & Innovation Team
Who You Need
World-class ML researchers, often with PhDs, who can work on frontier problems and build autonomous systems. This is the cutting edge—building agents, reinforcement learning systems, and solving problems that don't yet have established solutions.
Required Skills
- Reinforcement learning and agent design
- Multi-agent coordination and planning
- Research ability (reading papers, implementing novel techniques)
- Safety, alignment, and robustness engineering
- Advanced optimization and control theory
- Simulation and testing of autonomous systems
- Human-in-the-loop system design
- Publication-quality research and documentation
Team Size
20-50+ FTEs: 8-15 ML researchers/research engineers, 5-10 senior ML engineers, dedicated MLOps and infrastructure teams, plus extensive supporting roles. This is a research lab within your organization.
Internal vs External
Build world-class internal team or partner with frontier labs: Very few organizations can build Tier 5 capability internally—it requires top-tier talent and significant R&D investment. Most should partner with specialized labs (OpenAI, Anthropic, research universities) or buy proven solutions.
Training Existing Staff
Extremely difficult: Tier 5 requires research-caliber talent. You can't train most practitioners to this level—you need to hire from top PhD programs or recruit from leading AI labs. Exception: Your best Tier 4 engineers might grow into applied Tier 5 roles with years of experience.
Typical Roles
- ML Research Scientist (PhD): Develops novel algorithms
- Research Engineer: Implements research into production
- RL/Agent Specialist: Builds autonomous agents
- AI Safety Engineer: Ensures safe agent behavior
- Director of AI Research: Sets research agenda
- Chief AI Officer: Organization-wide AI strategy
Attempting Tier 5 when you haven't mastered Tier 2-4. Autonomous agents are the hardest AI problems—don't start here. Also, underestimating the talent scarcity; there are only thousands (not millions) of people qualified for Tier 5 work globally, and they're expensive.