- ML Engineering Excellence
- Deep ML Expertise: Advanced knowledge across multiple ML domains
- Production ML: Extensive experience building production-grade ML systems
- Architecture: Ability to design scalable, maintainable ML architectures
- MLOps: Strong understanding of ML infrastructure and operations
- LLM Systems: Experience with modern LLM-based applications and RAG
- Code Quality: Exemplary coding standards and best practices
- Technical Breadth
- Multiple ML Frameworks: Proficiency across TensorFlow, PyTorch, scikit-learn
- Cloud Platforms: Advanced AWS experience, familiarity with others
- Data Engineering: Understanding of data pipelines and infrastructure
- System Design: Ability to design complex distributed systems
- Performance Optimization: Experience optimizing ML models and infrastructure
- Software Engineering
- Clean Code: Writes exemplary, maintainable code
- Testing: Champions testing practices (unit, integration, ML-specific)
- Git & Collaboration: Advanced Git workflows and collaboration patterns
- CI/CD: Experience building and maintaining ML pipelines
- Documentation: Creates clear, comprehensive technical documentation
