ATC

Architecting multi-agent LLM workflows and responsible-AI guardrails for enterprise deployments.

Overview

Serving as Data Analyst & AI Engineer at ATC, where I architect prompt logic and multi-agent workflows for enterprise LLM deployments across knowledge retrieval and compliance use cases. My work spans agent orchestration, responsible-AI guardrails, and production observability — translating regulatory and business constraints into reliable autonomous systems.

The Challenge

Enterprise LLM deployments in regulated environments demand high inference accuracy, low hallucination rates, and strict policy adherence. The core challenge was building agentic systems that could make autonomous decisions within regulatory boundaries while handling real-world messiness: RBAC permission constraints, API sync lags, and long-tail edge cases that only surface in production.

The Solution

I architected prompt logic and multi-agent workflows across 7 enterprise LLM use cases, achieving 88% inference accuracy and reducing hallucination rates to 4% through 7 iterative evaluation cycles per use case. I deployed autonomous agents across 6 interaction paths with conditional routing and autonomous decision-making inside regulatory guardrails, delivering a 72% end-to-end task completion rate without human intervention. To keep systems reliable at scale, I engineered responsible-AI guardrails covering RBAC constraints and API sync lags (66% edge-case coverage) and stood up observability via SQL and Python analysis of interaction logs and evaluation traces to surface drift and failure patterns early.

My Thoughts

This role sits at the intersection of what I find most interesting in AI right now: making agentic systems genuinely trustworthy in high-stakes enterprise contexts. The discipline of iterative evaluation — seven cycles per use case, hallucination rates treated as a measurable KPI — is where good AI engineering diverges from demo-ware. It's reinforced my conviction that production ML is as much about observability and feedback loops as it is about model selection.

Key Achievements

• Achieved 88% inference accuracy and reduced hallucination rates to 4% across 7 enterprise LLM use cases
• Deployed autonomous agents across 6 interaction paths with 72% end-to-end task completion rate
• Engineered responsible-AI guardrails with 66% edge-case coverage for RBAC and API sync constraints
• Built observability pipelines in SQL and Python to surface drift and failure patterns in production
• Ran 7 iterative fine-tuning and evaluation cycles per use case to continuously improve agent reliability