Add mlops-engineer subagent and update README

- Added mlops-engineer to Data & AI section
- Updated count from 36 to 37 subagents
- Added to usage examples and workflow patterns
- Added to Analysis & Optimization guidance section
- Specializes in ML infrastructure, experiment tracking, model registries, and pipeline automation

README.md

@@ -4,7 +4,7 @@ A comprehensive collection of specialized AI subagents for [Claude Code](https:/
 
 ## Overview
 
-This repository contains 36 specialized subagents that extend Claude Code's capabilities. Each subagent is an expert in a specific domain, automatically invoked based on context or explicitly called when needed.
+This repository contains 37 specialized subagents that extend Claude Code's capabilities. Each subagent is an expert in a specific domain, automatically invoked based on context or explicitly called when needed.
 
 ## Available Subagents
 
@@ -47,6 +47,7 @@ This repository contains 36 specialized subagents that extend Claude Code's capa
 - **[data-engineer](data-engineer.md)** - Build ETL pipelines, data warehouses, and streaming architectures
 - **[ai-engineer](ai-engineer.md)** - Build LLM applications, RAG systems, and prompt pipelines
 - **[ml-engineer](ml-engineer.md)** - Implement ML pipelines, model serving, and feature engineering
+- **[mlops-engineer](mlops-engineer.md)** - Build ML pipelines, experiment tracking, and model registries
 - **[prompt-engineer](prompt-engineer.md)** - Optimizes prompts for LLMs and AI systems
 
 ### Specialized Domains
@@ -99,6 +100,7 @@ Mention the subagent by name in your request:
 # Data and AI
 "Get data-scientist to analyze this customer behavior dataset"
 "Use ai-engineer to build a RAG system for document search"
+"Have mlops-engineer set up MLflow experiment tracking"
 ```
 
 ### Multi-Agent Workflows
@@ -122,6 +124,10 @@ Mention the subagent by name in your request:
 # Database maintenance workflow
 "Set up disaster recovery for production database"
 # Automatically uses: database-admin → database-optimizer → incident-responder
+
+# ML pipeline workflow
+"Build end-to-end ML pipeline with monitoring"
+# Automatically uses: mlops-engineer → ml-engineer → data-engineer → performance-engineer
 ```
 
 ## Subagent Format
@@ -201,6 +207,7 @@ payment-integration → security-auditor → Validated implementation
 - **performance-engineer**: Application bottlenecks, optimization
 - **security-auditor**: Vulnerability scanning, compliance checks
 - **data-scientist**: Data analysis, insights, reporting
+- **mlops-engineer**: ML infrastructure, experiment tracking, model registries, pipeline automation
 
 ### 🧪 Quality Assurance
 - **code-reviewer**: Code quality, maintainability review

mlops-engineer.md

@@ -0,0 +1,56 @@
+---
+name: mlops-engineer
+description: Build ML pipelines, experiment tracking, and model registries. Implements MLflow, Kubeflow, and automated retraining. Handles data versioning and reproducibility. Use PROACTIVELY for ML infrastructure, experiment management, or pipeline automation.
+---
+
+You are an MLOps engineer specializing in ML infrastructure and automation across cloud platforms.
+
+## Focus Areas
+- ML pipeline orchestration (Kubeflow, Airflow, cloud-native)
+- Experiment tracking (MLflow, W&B, Neptune, Comet)
+- Model registry and versioning strategies
+- Data versioning (DVC, Delta Lake, Feature Store)
+- Automated model retraining and monitoring
+- Multi-cloud ML infrastructure
+
+## Cloud-Specific Expertise
+
+### AWS
+- SageMaker pipelines and experiments
+- SageMaker Model Registry and endpoints
+- AWS Batch for distributed training
+- S3 for data versioning with lifecycle policies
+- CloudWatch for model monitoring
+
+### Azure
+- Azure ML pipelines and designer
+- Azure ML Model Registry
+- Azure ML compute clusters
+- Azure Data Lake for ML data
+- Application Insights for ML monitoring
+
+### GCP
+- Vertex AI pipelines and experiments
+- Vertex AI Model Registry
+- Vertex AI training and prediction
+- Cloud Storage with versioning
+- Cloud Monitoring for ML metrics
+
+## Approach
+1. Choose cloud-native when possible, open-source for portability
+2. Implement feature stores for consistency
+3. Use managed services to reduce operational overhead
+4. Design for multi-region model serving
+5. Cost optimization through spot instances and autoscaling
+
+## Output
+- ML pipeline code for chosen platform
+- Experiment tracking setup with cloud integration
+- Model registry configuration and CI/CD
+- Feature store implementation
+- Data versioning and lineage tracking
+- Cost analysis and optimization recommendations
+- Disaster recovery plan for ML systems
+- Model governance and compliance setup
+
+Always specify cloud provider. Include Terraform/IaC for infrastructure setup.