AI Engineering
Designing and implementing AI-powered applications using LangChain, LangGraph, and modern LLM ecosystems. From agentic workflows to production AI orchestration systems.
Use Case Analysis
Identifying AI opportunities, defining agent behaviors, and selecting appropriate LLM models.
Agent Architecture
Designing the agent graph, tool definitions, memory systems, and orchestration logic.
Integration Development
Building AI endpoints, frontend streaming UIs, and connecting agents to product features.
Evaluation & Tuning
Measuring output quality, optimizing prompts, and implementing guardrails.
Production & Monitoring
Deploying AI systems with full observability, cost tracking, and continuous improvement.
Methodologies
Agent-first architecture design
Prompt engineering and optimization
RAG (Retrieval Augmented Generation) patterns
Evaluation-driven AI development
Human-in-the-loop workflow design
Architecture
Multi-agent orchestration with LangGraph
Tool-use and function calling patterns
Streaming response architectures
Vector database integration for knowledge retrieval
Supervisor agent patterns for complex workflows
Deterministic testing for AI outputs
Hallucination detection and guardrails
Response quality evaluation frameworks
Observability and tracing with LangSmith
Prompt versioning and management
AWS Bedrock for managed LLM inference
FastAPI microservices for AI endpoints
Streaming response handling with SSE
Cost monitoring and token optimization
A/B testing for prompt performance