I design and build production-oriented AI systems that combine agentic workflows, Retrieval-Augmented Generation (RAG), workflow automation, vector databases, and observability. My projects focus on reliable, maintainable AI pipelines that solve real operational problems while emphasizing security, evaluation, and production readiness.

Focus: AI workflow automation, Retrieval-Augmented Generation (RAG), multi-agent systems, knowledge pipelines, observability, technical documentation, and production-oriented AI architecture.

How to Use This Portfolio

This portfolio showcases production-oriented AI systems, workflow automation, Retrieval-Augmented Generation (RAG), and technical documentation developed to solve real operational problems. Each project demonstrates a different aspect of designing, building, documenting, and validating AI-powered workflows.

• Example 1 — HR Policy RAG Knowledge Specialist: End-to-end policy retrieval system featuring automated ingestion, semantic search, prompt guardrails, and grounded responses with citations.
• Example 2 — Multi-Agent Contract Analysis Platform: Event-driven AI workflow combining early-stage deduplication, specialized review agents, observability, operational monitoring, and production-oriented reliability practices.
• Example 3 — AI Content Operations Pipeline: Human-in-the-loop content generation system using deterministic prompt constraints, structured output formatting, and automated document delivery.
• Example 4 — Conversational Workflow Routing System: Natural-language intake system that classifies user intent, routes requests through decision logic, and escalates to human support when confidence thresholds are not met.

What This Demonstrates

• Designing agentic AI workflows with deterministic execution paths.
• Building Retrieval-Augmented Generation (RAG) systems from ingestion through response generation.
• Engineering semantic search pipelines using vector databases and reranking.
• Implementing workflow observability, monitoring, and operational reliability.
• Applying prompt guardrails to reduce hallucinations and improve grounded responses.
• Authoring technical documentation that supports implementation, troubleshooting, and maintainability.

Core Skills

AI Systems Engineering