AI Data Engineering
Data pipelines, feature stores, and embedding generation for AI/ML systems
Data pipelines, feature stores, and embedding generation for AI/ML systems
API design and implementation across REST, GraphQL, gRPC, and tRPC patterns
Authentication, authorization, and API security implementation
Deployment patterns from Kubernetes to serverless and edge functions
Design robust REST and GraphQL APIs with systematic guidance for architecture, versioning, and developer experience
Document database implementation for flexible schema applications
Graph database implementation for relationship-heavy data models
Navigation patterns and routing for frontend and backend applications
Search and filter interfaces for frontend and backend with debouncing and optimization
Data ingestion patterns for loading data from cloud storage, APIs, files, and streaming sources
Async communication patterns using message brokers and task queues
LLM and ML model deployment for inference
Monitoring, logging, and tracing with OpenTelemetry
Real-time communication patterns for live updates, collaboration, and presence
Relational database implementation across Python, Rust, Go, and TypeScript
Time-series database implementation for metrics, IoT, financial data, and observability
Vector database implementation for AI/ML applications, semantic search, and RAG systems