name: senior-computer-vision
description: World-class computer vision skill for image/video processing, object detection, segmentation, and visual AI systems. Expertise in PyTorch, OpenCV, YOLO, SAM, diffusion models, and vision transformers. Includes 3D vision, video analysis, real-time processing, and production deployment. Use when building vision AI systems, implementing object detection, training custom vision models, or optimizing inference pipelines.

Senior Computer Vision Engineer

World-class senior computer vision engineer skill for production-grade AI/ML/Data systems.

Quick Start

Main Capabilities

# Core Tool 1
python scripts/vision_model_trainer.py --input data/ --output results/

# Core Tool 2  
python scripts/inference_optimizer.py --target project/ --analyze

# Core Tool 3
python scripts/dataset_pipeline_builder.py --config config.yaml --deploy

Core Expertise

This skill covers world-class capabilities in:

• Advanced production patterns and architectures
• Scalable system design and implementation
• Performance optimization at scale
• MLOps and DataOps best practices
• Real-time processing and inference
• Distributed computing frameworks
• Model deployment and monitoring
• Security and compliance
• Cost optimization
• Team leadership and mentoring

Tech Stack

Languages: Python, SQL, R, Scala, Go
ML Frameworks: PyTorch, TensorFlow, Scikit-learn, XGBoost
Data Tools: Spark, Airflow, dbt, Kafka, Databricks
LLM Frameworks: LangChain, LlamaIndex, DSPy
Deployment: Docker, Kubernetes, AWS/GCP/Azure
Monitoring: MLflow, Weights & Biases, Prometheus
Databases: PostgreSQL, BigQuery, Snowflake, Pinecone

Reference Documentation

1. Computer Vision Architectures

Comprehensive guide available in references/computer_vision_architectures.md covering:

• Advanced patterns and best practices
• Production implementation strategies
• Performance optimization techniques
• Scalability considerations
• Security and compliance
• Real-world case studies

2. Object Detection Optimization

Complete workflow documentation in references/object_detection_optimization.md including:

• Step-by-step processes
• Architecture design patterns
• Tool integration guides
• Performance tuning strategies
• Troubleshooting procedures

3. Production Vision Systems

Technical reference guide in references/production_vision_systems.md with:

• System design principles
• Implementation examples
• Configuration best practices
• Deployment strategies
• Monitoring and observability

Production Patterns

Pattern 1: Scalable Data Processing

Enterprise-scale data processing with distributed computing:

• Horizontal scaling architecture
• Fault-tolerant design
• Real-time and batch processing
• Data quality validation
• Performance monitoring

Pattern 2: ML Model Deployment

Production ML system with high availability:

• Model serving with low latency
• A/B testing infrastructure
• Feature store integration
• Model monitoring and drift detection
• Automated retraining pipelines

Pattern 3: Real-Time Inference

High-throughput inference system:

• Batching and caching strategies
• Load balancing
• Auto-scaling
• Latency optimization
• Cost optimization

Best Practices

Development

• Test-driven development
• Code reviews and pair programming
• Documentation as code
• Version control everything
• Continuous integration

Production

• Monitor everything critical
• Automate deployments
• Feature flags for releases
• Canary deployments
• Comprehensive logging

Team Leadership

• Mentor junior engineers
• Drive technical decisions
• Establish coding standards
• Foster learning culture
• Cross-functional collaboration

Performance Targets

Latency:
- P50: < 50ms
- P95: < 100ms
- P99: < 200ms

Throughput:
- Requests/second: > 1000
- Concurrent users: > 10,000

Availability:
- Uptime: 99.9%
- Error rate: < 0.1%

Security & Compliance

• Authentication & authorization
• Data encryption (at rest & in transit)
• PII handling and anonymization
• GDPR/CCPA compliance
• Regular security audits
• Vulnerability management

Common Commands

# Development
python -m pytest tests/ -v --cov
python -m black src/
python -m pylint src/

# Training
python scripts/train.py --config prod.yaml
python scripts/evaluate.py --model best.pth

# Deployment
docker build -t service:v1 .
kubectl apply -f k8s/
helm upgrade service ./charts/

# Monitoring
kubectl logs -f deployment/service
python scripts/health_check.py

Resources

• Advanced Patterns: references/computer_vision_architectures.md
• Implementation Guide: references/object_detection_optimization.md
• Technical Reference: references/production_vision_systems.md
• Automation Scripts: scripts/ directory

Senior-Level Responsibilities

As a world-class senior professional:

1. Technical Leadership
2. Drive architectural decisions
3. Mentor team members
4. Establish best practices

5. Ensure code quality

6. Strategic Thinking

7. Align with business goals
8. Evaluate trade-offs
9. Plan for scale

10. Manage technical debt

11. Collaboration

12. Work across teams
13. Communicate effectively
14. Build consensus

15. Share knowledge

16. Innovation

17. Stay current with research
18. Experiment with new approaches
19. Contribute to community

20. Drive continuous improvement

21. Production Excellence

22. Ensure high availability
23. Monitor proactively
24. Optimize performance
25. Respond to incidents