AI Systems Engineering
Design, development, and deployment of production-grade AI-driven software systems, machine learning models, and intelligent automation pipelines.
- End-to-end LLM application development using the Anthropic, OpenAI, and open-source model ecosystems
- Multi-agent system design using LangGraph, AutoGen, and custom orchestration frameworks
- Retrieval-Augmented Generation (RAG) pipelines with hybrid search and reranking
- MLOps infrastructure: model serving, versioning, monitoring, and continuous evaluation
- AI agent tool integration: web search, code execution, database access, external APIs
- Production hardening: rate limiting, cost controls, fallback chains, and observability