The AI Engineering Team Problem

You need to build AI. You hire machine learning engineers. They struggle. Projects stall. You don’t understand why.

The problem: AI engineering is not machine learning. ML engineers are trained to optimize models. AI engineers need to ship systems.

This distinction matters. A world-class ML engineer who’s optimized models for academic papers might be terrible at building production AI systems. They optimize the wrong metrics. They don’t understand deployment constraints. They don’t think about inference costs or latency.

Building high-performing AI teams requires understanding the specific roles, skills, and team structures that actually work in production.

The AI Engineering Landscape

First, let’s clarify what we’re building:

Traditional ML/Data Science:

• Historical data analysis
• Pattern discovery
• Model optimization
• Batch predictions
• Offline analysis

AI Engineering (LLM-based):

• Real-time systems
• Knowledge integration
• Prompt optimization
• Agent systems
• Production deployment
• Continuous improvement

These require different skills and mindsets.

Core AI Engineering Roles

1. AI Product Manager

Responsibility: Define what the AI system should do and why.

Key activities:

• Identify use cases that benefit from AI
• Define success metrics
• Understand user needs
• Prioritize features
• Manage stakeholder expectations

Required skills:

• Product thinking (not just technical)
• Understanding of AI capabilities and limitations
• User empathy
• Business acumen
• Communication

Hiring profile:

• 3-5 years product management experience
• Experience shipping software products
• Curiosity about AI (doesn’t need to be technical)
• Ability to learn quickly
• Strong communication

Red flags:

• “We need to use AI because it’s trendy”
• Doesn’t understand the actual problem being solved
• No experience shipping products
• Purely technical background with no product sense

Compensation: $150K-250K (senior product managers command higher)

2. AI Engineer / Prompt Engineer

Responsibility: Build AI systems using existing models and tools.

Key activities:

• Prompt engineering and optimization
• RAG system design
• Agent system design
• Integration with tools and APIs
• System testing and evaluation
• Observability and monitoring

Required skills:

• Understanding of LLM capabilities and limitations
• Prompt engineering expertise
• Software engineering fundamentals
• System design thinking
• Testing and validation
• Python or similar language
• No ML background required

Hiring profile:

• Software engineer with interest in AI
• Or ML engineer willing to learn software engineering
• 2-5 years software engineering experience
• Strong fundamentals (algorithms, data structures, system design)
• Ability to write clean, maintainable code

Red flags:

• Only academic ML experience
• Hasn’t shipped production systems
• Doesn’t understand software engineering basics
• Can’t explain their thinking clearly

Compensation: $130K-220K

Why this role matters: Most AI engineering work is not ML. It’s system design, integration, and optimization. You need people who think like software engineers, not data scientists.

3. ML Engineer

Responsibility: Optimize models and implement advanced ML techniques.

Key activities:

• Fine-tuning models
• Custom model training
• Advanced RAG techniques
• Embedding model optimization
• Model evaluation and benchmarking
• Research and experimentation

Required skills:

• Deep ML knowledge
• PyTorch or TensorFlow
• Training infrastructure
• Evaluation methodology
• Statistical thinking
• Research skills

Hiring profile:

• 3-7 years ML experience
• Published research or proven track record
• Strong math background
• Experience training models in production
• Understanding of ML ops

Red flags:

• Only academic research experience
• Can’t explain trade-offs clearly
• Hasn’t dealt with production constraints
• Perfectionist (good is good enough in production)

Compensation: $150K-280K (senior ML engineers command premium)

When you need this role:

• Using custom models (not just APIs)
• Fine-tuning for specific domains
• Performance optimization critical
• Advanced RAG techniques needed

When you don’t:

• Using off-the-shelf models (GPT-4, Claude)
• Simple RAG systems
• Prompt engineering sufficient

4. AI Infrastructure / ML Ops Engineer

Responsibility: Build systems to train, deploy, and monitor AI.

Key activities:

• Model serving infrastructure
• Experiment tracking
• Model versioning
• Deployment pipelines
• Monitoring and alerting
• Cost optimization
• Security and compliance

Required skills:

• DevOps/infrastructure expertise
• Containerization (Docker, Kubernetes)
• CI/CD pipelines
• Monitoring and observability
• Infrastructure as code
• Database and storage systems
• Security fundamentals

Hiring profile:

• 4-8 years DevOps/infrastructure experience
• Experience with ML infrastructure (optional but helpful)
• Strong fundamentals in systems
• Problem-solving mindset
• Attention to operational details

Red flags:

• No production infrastructure experience
• Doesn’t understand monitoring and alerting
• Can’t think about cost and efficiency
• Only theoretical knowledge

Compensation: $140K-240K

When you need this role:

• Multiple models in production
• Complex deployment requirements
• High-scale systems (millions of queries)
• Compliance requirements

When you don’t:

• Using managed services (OpenAI API, Anthropic API)
• Single model deployment
• Low-scale systems

5. Data Engineer

Responsibility: Build systems to prepare and manage data.

Key activities:

• Data pipeline design
• ETL/ELT implementation
• Data quality monitoring
• Feature engineering
• Data warehouse management
• Document processing for RAG

Required skills:

• SQL and data modeling
• Python or similar language
• Data pipeline tools (Airflow, dbt, etc.)
• Data warehouse systems
• Data quality thinking
• Performance optimization

Hiring profile:

• 3-7 years data engineering experience
• Experience with data pipelines
• SQL expertise
• Understanding of data quality
• Problem-solving mindset

Red flags:

• Only theoretical knowledge
• Hasn’t managed large datasets
• Doesn’t understand data quality
• Can’t optimize for performance

Compensation: $130K-220K

When you need this role:

• Complex data pipelines
• Large datasets
• Real-time data processing
• Data quality critical

When you don’t:

• Using managed data services
• Small datasets
• Simple data sources

Team Structures by Scale

Startup (1-3 people)

Team composition:

• 1 AI Product Manager
• 1-2 AI Engineers (generalists)

Responsibilities overlap heavily:

• Product manager also does some engineering
• Engineers also think about product

Tools:

• Managed services (OpenAI, Anthropic)
• No-code tools (Langflow, Chat Studio)
• Minimal infrastructure

Focus:

• Validate use case
• Build MVP
• Measure product-market fit

Early Stage (4-10 people)

Team composition:

• 1 AI Product Manager
• 2-4 AI Engineers
• 1 ML Engineer (optional)
• 1 Data Engineer (part-time or shared)

Structure:

Product Manager
    ├─ AI Engineer (RAG/Agents)
    ├─ AI Engineer (Integration)
    ├─ ML Engineer (optional, for fine-tuning)
    └─ Data Engineer (part-time)

Focus:

• Ship production systems
• Optimize performance
• Build operational maturity
• Scale to meaningful users

Growth Stage (10-30 people)

Team composition:

• 1 AI Product Manager (or 2 if multiple products)
• 5-10 AI Engineers
• 2-3 ML Engineers
• 1-2 ML Ops Engineers
• 1-2 Data Engineers

Structure:

VP AI/ML
├─ AI Product Manager
│   ├─ Senior AI Engineer
│   ├─ AI Engineer (RAG specialist)
│   ├─ AI Engineer (Agents specialist)
│   └─ AI Engineer (Integration)
├─ ML Engineering Lead
│   ├─ ML Engineer (fine-tuning)
│   └─ ML Engineer (research)
├─ ML Ops Lead
│   └─ ML Ops Engineer
└─ Data Engineering Lead
    ├─ Data Engineer (pipelines)
    └─ Data Engineer (quality)

Focus:

• Multiple products
• Advanced optimization
• Operational excellence
• Cost efficiency
• Compliance and security

Enterprise (30+ people)

Team composition:

• VP AI/ML
• Multiple product teams (each with PM + engineers)
• Centralized ML Engineering team
• Centralized ML Ops team
• Centralized Data Engineering team
• Research team

Structure:

VP AI/ML
├─ Product Organization
│   ├─ Product Manager (Product A)
│   │   ├─ Senior AI Engineer
│   │   ├─ AI Engineer
│   │   └─ AI Engineer
│   ├─ Product Manager (Product B)
│   │   ├─ Senior AI Engineer
│   │   ├─ AI Engineer
│   │   └─ AI Engineer
│   └─ Product Manager (Product C)
├─ ML Engineering (Central)
│   ├─ ML Engineering Lead
│   ├─ ML Engineer (fine-tuning)
│   ├─ ML Engineer (evaluation)
│   └─ ML Engineer (research)
├─ ML Ops (Central)
│   ├─ ML Ops Lead
│   ├─ ML Ops Engineer
│   └─ ML Ops Engineer
├─ Data Engineering (Central)
│   ├─ Data Engineering Lead
│   ├─ Data Engineer
│   └─ Data Engineer
└─ Research
    ├─ Research Lead
    └─ Research Scientists

Focus:

• Multiple concurrent products
• Shared infrastructure
• Advanced optimization
• Research and innovation
• Enterprise compliance

Critical Skills by Role

AI Engineer Core Skills

Essential (must have):

• Software engineering fundamentals
• Python proficiency
• Understanding of LLM capabilities
• Prompt engineering experience
• System design thinking
• Testing and debugging

Important (should have):

• RAG system design
• Agent system design
• API integration
• Database understanding
• Basic ML concepts
• Performance optimization

Nice to have:

• ML background
• DevOps experience
• Data engineering knowledge
• Research publication

ML Engineer Core Skills

Essential (must have):

• ML theory (loss functions, optimization, etc.)
• Deep learning frameworks (PyTorch, TensorFlow)
• Training and evaluation methodology
• Mathematical thinking
• Research skills

Important (should have):

• Fine-tuning experience
• Evaluation frameworks
• Statistical thinking
• Performance optimization
• Production experience

Nice to have:

• Research publications
• Advanced techniques (LoRA, quantization)
• Distributed training
• Specialized domain knowledge

Skill Gaps to Watch For

Common ML → AI transition gaps:

• Over-optimizing (good enough is good enough)
• Ignoring inference cost
• Not thinking about latency
• Perfectionism delaying shipping
• Academic mindset vs. product mindset

Common Software Engineer → AI gaps:

• Underestimating ML complexity
• Expecting deterministic behavior
• Not understanding evaluation
• Treating AI like traditional software
• Insufficient testing mindset

Common gaps across team:

• No one thinking about product
• No one optimizing costs
• No one focused on observability
• No one understanding deployment constraints
• No one thinking about user experience

Building Team Culture

Psychological Safety

• Encourage experimentation
• Normalize failure (model doesn’t work, that’s data)
• Celebrate learning
• No blame culture

Shared Understanding

• Regular knowledge sharing
• Document decisions
• Cross-functional pairing
• Weekly syncs on progress

Metrics Alignment

• Everyone understands success metrics
• Not just accuracy (cost, latency, user satisfaction matter)
• Regular metrics reviews
• Transparent about what’s working/not

Continuous Learning

• Budget for learning (conferences, courses, books)
• Time for experimentation
• Research time (10-20% of time)
• Encourage publication and speaking

Hiring Checklist

For AI Engineer positions:

• Strong software engineering fundamentals
• Can explain their past projects clearly
• Curious about AI (even if no experience)
• Has shipped production systems
• Comfortable with ambiguity
• Collaborative mindset
• Problem-solving approach (not just coding)

For ML Engineer positions:

• Deep ML knowledge (can explain concepts)
• Production experience (not just academic)
• Trade-off thinking (not perfection)
• Clear communication
• Research mindset
• Pragmatic approach

For Product Manager positions:

• Product shipping experience
• User empathy
• Analytical thinking
• Curiosity about AI
• Communication skills
• Business acumen

Onboarding New Team Members

Week 1:

• Product orientation (what are we building?)
• System architecture overview
• Codebase walkthrough
• First small task

Week 2-3:

• Deeper technical dives
• Pair programming sessions
• Understand evaluation methodology
• Complete first feature

Week 4:

• Understand team processes
• Contribute to team goals
• Build relationships
• Ramp-up complete

First 3 months:

• Ship first independent feature
• Understand team culture
• Build credibility
• Identify improvement areas

Common Team Mistakes

Mistake 1: Hiring only ML researchers

• Problem: Over-optimize, slow shipping, ignore costs
• Solution: Mix of ML engineers and software engineers

Mistake 2: No product thinking

• Problem: Build technically impressive but useless systems
• Solution: Hire strong product manager

Mistake 3: Ignoring operations

• Problem: Systems work in development, fail in production
• Solution: Invest in ML Ops and monitoring from start

Mistake 4: No data expertise

• Problem: Garbage in, garbage out
• Solution: Invest in data engineering

Mistake 5: Isolated teams

• Problem: Duplicated work, poor decisions, slow progress
• Solution: Strong communication and knowledge sharing

Compensation and Retention

Market rates (2025, US):

Plus:

• Equity (important for startups)
• Benefits (health, 401k, etc.)
• Remote/flexible work
• Learning budget
• Conference budget

Retention factors:

• Clear growth path
• Interesting problems
• Learning opportunities
• Good management
• Competitive compensation
• Team culture
• Product impact

Moving Forward

Building AI teams is different from building ML teams. You need:

1. Mixed expertise: Software engineers + ML engineers + product managers
2. Right roles: Understand what each role actually does
3. Team structure: Match to your scale and complexity
4. Culture: Psychological safety, learning, collaboration
5. Hiring: Look for the right mindset, not just credentials

Most importantly: hire for shipping, not for perfection.

The best AI teams ship products that users love. They optimize for impact, not for academic papers.

Build your AI team with Calliope →