AI Integration
AI-Powered Systems
Integrating LLMs into production applications with resilience patterns, cost awareness, and system design principles.
LLM Integration Architecture
Multi-model orchestration, fallback chains, and prompt template management
AI-Aware Infrastructure
Circuit breakers, tiered retries, and cost-optimized request routing
Context Management
Token budgeting, conversation state, and context injection middleware
Streaming & Real-Time
SSE protocols, chunked responses, and progressive rendering
Vector Operations
Embedding pipelines, semantic search, and similarity ranking
AI Safety & Governance
Content filtering, PII detection, and rate-limit enforcement
Competency Map
Research-driven exploration of AI capabilities with production engineering mindset.
01
Applied AI Research
Active— current focusExploring LLM capabilities and integration patterns
Prompt engineeringMulti-model evaluationClaude API (Anthropic)OpenAI API patternsResponse streamingToken optimizationModel selection heuristicsPython research scripts
02
Production AI Systems
ActiveBuilding resilient AI-powered applications
Circuit breaker patternsExponential backoffMacro/micro-level retriesContext injection middlewareEdge function deploymentCost monitoringLatency budgetingFallback strategies
03
AI Platform Engineering
DevelopingScalable infrastructure for AI features
Vector databasesEmbedding cachingBatch processingConversation persistenceA/B testing frameworksModel observabilityCompliance guardrailsMulti-tenant AI isolation