What a Principal } should know

Defines algorithmic strategy for ML platform. Researches novel algorithms for specific ML tasks. Publishes optimization results at conferences.

Defines code quality strategy for ML organization. Creates ML-specific coding standards. Introduces industry best practices.

Designs data abstractions at ML platform level. Researches novel data structures for ML tasks (LSH, HNSW indexes). Establishes standards for the entire organization.

Defines ML platform architectural strategy. Designs ML platform API with extensibility in mind. Influences open-source ML frameworks.

Defines ML API strategy at platform level. Designs API architecture for ML platform. Evaluates REST vs gRPC vs streaming for ML serving.

Defines ML API strategy for the platform. Evaluates REST vs gRPC vs streaming for ML. Designs API architecture for enterprise ML serving.

Defines containerization strategy for enterprise ML. Evaluates container runtimes for ML workloads. Designs container orchestration for ML platform.

Defines infrastructure strategy for enterprise ML. Evaluates managed Kubernetes vs self-managed for ML. Designs multi-cluster ML architecture.

Defines ML testing strategy for enterprise. Designs ML quality assurance framework. Evaluates ML testing tools.

Defines distributed processing strategy for enterprise ML. Designs data processing layer for ML platform. Evaluates novel distributed frameworks.

Defines enterprise data quality strategy for ML. Designs data governance for ML platform. Evaluates data quality tools and frameworks.

Defines data processing strategy for ML platform. Evaluates novel data processing frameworks. Designs unified data API for ML.

Defines data pipeline strategy for ML platform. Evaluates ETL vs ELT vs streaming for ML. Designs data architecture for enterprise ML.

Defines ML modeling strategy for the organization. Evaluates novel classical ML approaches. Establishes best practices for production ML systems.

Defines experimentation strategy for enterprise. Designs experiment platform. Evaluates novel experimentation approaches.

Defines feature engineering strategy for enterprise. Designs feature platform. Evaluates novel approaches to feature management.

Defines tabular ML strategy for the organization. Researches novel gradient boosting approaches. Publishes results at conferences.

Defines ML pipeline platform strategy. Evaluates pipeline orchestrators. Designs enterprise ML pipeline architecture.

Defines ML platform strategy. Designs enterprise experiment tracking. Evaluates and integrates ML lifecycle tools.

Defines model observability strategy for enterprise. Designs ML observability platform. Evaluates monitoring technologies.

Defines enterprise model serving strategy. Evaluates serving technologies. Designs multi-model serving platform.

Defines deep learning strategy for enterprise. Researches novel architectures. Optimizes GPU infrastructure costs. Publishes results.

Defines code review culture for ML organization. Conducts cross-team architecture reviews. Establishes review standards.

Defines version control strategy for enterprise ML. Designs repository architecture for ML platform. Evaluates tools for ML version control.