Data & AI subagents are your specialists in the world of data engineering, machine learning, and artificial intelligence. These experts handle everything from building robust data pipelines to training sophisticated ML models, from optimizing databases to deploying AI systems at scale. They bridge the gap between raw data and intelligent applications, ensuring your data-driven solutions are efficient, scalable, and impactful.

<¯ When to Use Data & AI Subagents

Use these subagents when you need to:

Build data pipelines for ETL/ELT workflows
Train machine learning models for predictions and insights
Design AI systems for production deployment
Optimize database performance at scale
Implement NLP solutions for text processing
Create computer vision applications
Deploy ML models with MLOps best practices
Analyze data for business insights

=Ë Available Subagents

ai-engineer - AI system design and deployment expert

AI systems specialist building production-ready artificial intelligence solutions. Masters model deployment, scaling, and integration. Bridges the gap between AI research and real-world applications.

Use when: Deploying AI models to production, designing AI system architectures, integrating AI into applications, scaling AI services, or implementing AI pipelines.

data-analyst - Data insights and visualization specialist

Analytics expert transforming data into actionable insights. Masters statistical analysis, data visualization, and business intelligence tools. Tells compelling stories with data.

Use when: Analyzing business data, creating dashboards, performing statistical analysis, building reports, or discovering data insights.

data-engineer - Data pipeline architect

Data infrastructure specialist building scalable data pipelines. Expert in ETL/ELT processes, data warehousing, and streaming architectures. Ensures data flows reliably from source to insight.

Use when: Building data pipelines, designing data architectures, implementing ETL processes, setting up data warehouses, or handling big data processing.

data-scientist - Analytics and insights expert

Data science practitioner combining statistics, machine learning, and domain expertise. Masters predictive modeling, experimentation, and advanced analytics. Extracts value from complex datasets.

Use when: Building predictive models, conducting experiments, performing advanced analytics, developing ML algorithms, or solving complex data problems.

database-optimizer - Database performance specialist

Database performance expert ensuring queries run at lightning speed. Masters indexing strategies, query optimization, and database tuning. Makes databases perform at their peak.

Use when: Optimizing slow queries, designing efficient schemas, implementing indexing strategies, tuning database performance, or scaling databases.

llm-architect - Large language model architect

LLM specialist designing and deploying large language model solutions. Expert in prompt engineering, fine-tuning, and LLM applications. Harnesses the power of modern language models.

Use when: Implementing LLM solutions, designing prompt strategies, fine-tuning models, building chatbots, or creating AI-powered applications.

machine-learning-engineer - Machine learning systems expert

ML engineering specialist building end-to-end machine learning systems. Masters the entire ML lifecycle from data to deployment. Ensures models work reliably in production.

Use when: Building ML pipelines, implementing ML systems, deploying models, creating ML infrastructure, or productionizing ML solutions.

ml-engineer - Machine learning specialist

Machine learning expert developing and optimizing ML models. Proficient in various algorithms, frameworks, and techniques. Solves complex problems with machine learning.

Use when: Training ML models, selecting algorithms, optimizing model performance, implementing ML solutions, or experimenting with new techniques.

mlops-engineer - MLOps and model deployment expert

MLOps specialist ensuring smooth ML model deployment and operations. Masters CI/CD for ML, model monitoring, and versioning. Brings DevOps practices to machine learning.

Use when: Setting up ML pipelines, implementing model monitoring, automating ML workflows, managing model versions, or establishing MLOps practices.

nlp-engineer - Natural language processing expert

NLP specialist building systems that understand and generate human language. Expert in text processing, language models, and linguistic analysis. Makes machines understand text.

Use when: Building text processing systems, implementing chatbots, analyzing sentiment, extracting information from text, or developing language understanding features.

postgres-pro - PostgreSQL database expert

PostgreSQL specialist mastering advanced features and optimizations. Expert in complex queries, performance tuning, and PostgreSQL-specific capabilities. Unlocks PostgreSQL's full potential.

Use when: Working with PostgreSQL, optimizing Postgres queries, implementing advanced features, designing PostgreSQL schemas, or troubleshooting Postgres issues.

prompt-engineer - Prompt optimization specialist

Prompt engineering expert crafting effective prompts for AI models. Masters prompt design, testing, and optimization. Maximizes AI model performance through strategic prompting.

Use when: Designing prompts for LLMs, optimizing AI responses, implementing prompt strategies, testing prompt effectiveness, or building prompt-based applications.

=€ Quick Selection Guide

If you need to...	Use this subagent
Deploy AI systems	ai-engineer
Analyze business data	data-analyst
Build data pipelines	data-engineer
Create ML models	data-scientist
Optimize databases	database-optimizer
Work with LLMs	llm-architect
Build ML systems	machine-learning-engineer
Train ML models	ml-engineer
Deploy ML models	mlops-engineer
Process text data	nlp-engineer
Optimize PostgreSQL	postgres-pro
Design AI prompts	prompt-engineer

=¡ Common Data & AI Patterns

End-to-End ML System:

data-engineer for data pipeline
data-scientist for model development
ml-engineer for model optimization
mlops-engineer for deployment

AI Application:

llm-architect for LLM integration
prompt-engineer for prompt optimization
ai-engineer for system design
nlp-engineer for text processing

Data Platform:

data-engineer for infrastructure
database-optimizer for performance
postgres-pro for PostgreSQL
data-analyst for insights

Production ML:

machine-learning-engineer for ML systems
mlops-engineer for operations
ai-engineer for deployment
data-engineer for data flow

<¬ Getting Started

Define your data/AI objectives clearly
Assess your data landscape and requirements
Choose appropriate specialists for your needs
Provide data context and constraints
Follow best practices for implementation

=Ú Best Practices

Start with data quality: Good models need good data
Iterate quickly: ML is experimental by nature
Monitor everything: Models drift, data changes
Version control: Track data, code, and models
Document thoroughly: ML systems are complex
Test rigorously: Validate models before production
Scale gradually: Start small, prove value
Stay ethical: Consider AI's impact

Choose your data & AI specialist and unlock the power of your data today!