Jason McGinty | AVP & Senior Database Systems Engineer

Engineering Data Systems with Precision

Jason McGinty is a high-impact Senior Database Systems Engineer and banking-sector leader with 20+ years of expertise designing, securing, migrating, and operating enterprise SQL Server environments. His proven track record includes delivering $600K+ in documented cost savings at U.S. Bank through infrastructure consolidation and intelligent licensing optimization.

As Assistant Vice President at U.S. Bank, Jason executes near zero-downtime AWS EC2 and Azure cloud migrations while driving enterprise-wide cybersecurity hardening programs. He combines deep technical database architecture with cross-functional AVP-level leadership, advanced PowerShell and T-SQL automation, and hands-on Python-based AI agent development for business applications.

Currently advancing his expertise through the AI Agents in Business Applications post-graduate certificate program at the University of Texas at Austin, Jason applies cutting-edge techniques — including Retrieval-Augmented Generation (RAG), LangGraph multi-agent orchestration, and LLM integration — to solve real-world enterprise challenges.

20+

Years of Experience

$600K

Cost Savings Delivered

AVP

U.S. Bank Leadership

LLM Multi-Agentic Architecture

Core Competencies

🤖

AI & Machine Learning

Retrieval-Augmented Generation (RAG)
LangChain & LangGraph Orchestration
OpenAI GPT-4o / Groq LLM Integration
Vector Embeddings & ChromaDB
Multi-Agent ReAct Reasoning

🗃

Database Engineering

SQL Server Architecture (through 2025)
High-Availability & Clustering
Schema Design & Capacity Planning
T-SQL Performance Diagnostics
Replication & Data Pipeline Design

☁

Cloud & Infrastructure

AWS EC2 Cloud Migration
Microsoft Azure Migration
Data Center Consolidation
SQL Server PolyBase & Hadoop
Enterprise Data Lake Design

🛠

Automation & Programming

Python OOP & AI Agent Frameworks
PowerShell Automation & Scripting
SSIS Development
Self-Healing Monitoring Systems
Shell Scripting

🛡

Security & Compliance

Defense-in-Depth Cybersecurity Hardening
Certificate-Based Security Frameworks
SQL Injection Prevention
Insider-Threat Exposure Reduction
24/7 Production Operations

📈

Executive Leadership

Cost Reduction & Licensing Optimization
Cross-Functional Team Leadership
Resource Management
Developer Mentoring & Coaching
SLA-Driven Incident Resolution

👥

Enterprise Architecture

Mission-Critical System Design
Infrastructure Consolidation
Capacity Planning & Forecasting
High-Volume Production Environments
Cross-System Data Integration

📊

Business Intelligence

Strategic Data Analysis
Actionable Insights Reporting
LangSmith Monitoring & Tracing
Metrics & Graphing Pipelines
Enterprise Workflow Automation

Professional Experience

Over two decades of enterprise database engineering, cloud infrastructure leadership, and AI-driven automation across banking, professional services, and media industries.

USB

U.S. Bank

Atlanta, GA

Feb 2016 – Present

Assistant Vice President | Senior Database Systems Engineer

Delivered ~$500K in storage cost savings by migrating legacy architectures to an agile, cloud-aligned storage provider, eliminating over-provisioned infrastructure and reducing operational overhead.
Reduced Microsoft licensing expenditure by $100K through systematic resource analysis and rightsizing of CPU core allocations across the enterprise SQL Server estate.
Performed the migration of a mission-critical database platform to AWS EC2 and separately transitioned multiple high-volume systems to Microsoft Azure with near zero-downtime production availability.
Architected an enterprise data lake using SQL Server PolyBase and Hadoop, enabling advanced analytics and cross-system data access at scale.
Spearheaded a comprehensive defense-in-depth cybersecurity hardening program across the SQL Server environment, significantly reducing the organization's attack surface.
Diagnosed and resolved critical T-SQL performance issues affecting global branch offices; led cross-functional resolution of complex, multi-department infrastructure incidents under tight SLAs.
Sustained 24/7/365 production operations for hundreds of SQL Server instances — earning multiple internal performance awards.

SQL Server AWS EC2 Azure PolyBase Cybersecurity T-SQL

KPMG

KPMG US

Atlanta Metropolitan Area, GA

Feb 2012 – Feb 2016

Senior Database Administrator

Architected a certificate-based security framework replacing direct end-user database access with a secure application model, materially reducing insider-threat exposure.
Built a fully automated self-healing monitoring system that resolved common SQL Server infrastructure issues autonomously, reducing manual intervention by an estimated 35–40%.
Established annual disk growth planning tied directly to storage expenditure forecasts, improving corporate budget accuracy and eliminating unplanned capacity shortfalls.
Coached and mentored cross-functional development teams on T-SQL best practices and database design, cutting performance-related incidents from application code.

SQL Server PowerShell Security Automation Mentoring

Thomson Reuters

Atlanta Metropolitan Area, GA

Feb 2011 – Feb 2012

Senior Database Engineer

Supported a high-availability SQL Server clustered production environment (later acquired by KPMG).
Developed robust PowerShell monitoring scripts to streamline health checks and participated in developer code and architecture reviews.

SQL Server High Availability PowerShell Clustering

⋮

Earlier Career

Various Enterprise Environments

Prior to 2011

Senior Database Administrator

Managed enterprise production environments, handling full-lifecycle migrations, deployments, patching, and 24/7 production support across multiple organizations.

Portfolio

Featured Projects

The following projects were developed as part of the AI Agents in Business Applications post-graduate certificate program at the University of Texas at Austin. Each demonstrates applied expertise in AI engineering, database systems, and enterprise automation.

RAG LLM Vector Search Python

Apple HBR Report — RAG Intelligence System

Developed a production-grade Retrieval-Augmented Generation (RAG) application designed to help business analysts at firms like Andreessen Horowitz efficiently extract strategic insights from dense enterprise reports. The system ingests the Harvard Business Review article "How Apple Is Organized for Innovation" (Podolny & Hansen, 2021) into a vector store and enables natural-language querying without manual document review.

Business Context

As organizations scale, analysts face information overload from large volumes of reports and research documents. This system demonstrates how Semantic Search and RAG models can dramatically reduce time-to-insight, enabling analysts to focus on higher-level strategic decision-making rather than information retrieval.

Technical Approach

The pipeline loads an 11-page PDF into a vector database using cosine similarity embeddings, then compares LLM-only responses against RAG-augmented responses across three structured business questions. The Groq LLM integration demonstrates how RAG produces more precise, document-grounded answers versus generic LLM output.

Key Findings

RAG with vector processing is more concise and specific than standalone LLM due to cosine similarity scoring of vectorized data
LLM-only responses can be incorrect, verbose, or produce no answer when document context is absent
Apple's flat management structure enables robust cross-departmental decision-making
Horizontal organizational design requires leaders who collaborate and integrate effectively

Business Recommendations

Build a horizontal management structure that fosters high collaboration between departments
Instill a company culture where product quality is paramount
Hire leaders with open and honest communication skills and AI literacy
Deploy RAG over standalone LLM for document-specific enterprise Q&A applications

Python 3 Groq LLM Vector Embeddings Cosine Similarity PDF Ingestion RAG Pipeline

Full Project List →

LangGraph GPT-4o SQLite Healthcare AI

AI-Powered EHR Assistant — Clinical Intelligence Agent

Engineered a professional Electronic Health Record (EHR) Assistant Agent using LangGraph's ReAct reasoning framework. The system enables patients to query their own health records through natural language while enforcing strict safety guardrails, patient data scoping, and clinical escalation protocols. Built on GPT-4o-mini with a custom SQLite toolkit factory and policy-driven safety nodes.

Architecture Overview

The agent operates against a structured SQLite database containing six clinical tables: patients, encounters, clinical notes, labs, medications, and allergies. A custom EHRToolkit factory generates nine LangChain tools, each enforcing parameterized queries to prevent SQL injection. Patient scoping is enforced at the execution node level to prevent cross-patient data access.

Safety & Compliance Design

A dedicated policy_route tool evaluates every user query against a safety rules dataset, classifying responses as answer, escalate_clinician, escalate_emergency, or refuse. Emergency cases trigger immediate escalation templates. The system includes full ReAct message trace logging for audit and grading purposes.

Key Findings

AI assistant provides strong informational support for patients on lab results and medications
Accuracy requires validation — data quality and patient ID matching must be robust
Emergency escalation language must be explicit (e.g., "Dial 911 immediately") rather than passive
Multi-agent validation layer would improve response accuracy and reliability

Business Recommendations

Add a secondary validation agent for multi-agent accuracy verification
Include explicit disclaimers that AI is informational only and not a substitute for medical diagnosis
Integrate emergency escalation with phone/app services for real-time response
Expand the safety rules dataset to cover additional clinical edge cases

LangGraph GPT-4o-mini SQLite LangChain Tools ReAct Reasoning Safety Guardrails Pydantic

Full Project List →

Multi-Agent LangGraph ChromaDB Logistics AI

AI Delivery Exception Automation — Last-Mile Multi-Agent System

Designed and implemented a multi-agent delivery processing system for last-mile logistics exception handling. The system ingests shipment records, identifies delivery exceptions, and autonomously resolves or escalates cases using a LangGraph state machine with resolution and critic nodes. Includes performance metrics, graphing pipelines, and a basic prompt injection security check.

System Architecture

The pipeline processes shipping records through a multi-agent LangGraph workflow featuring dedicated resolution and critic nodes. ChromaDB provides vector-based retrieval for policy and routing decisions. The system integrates LangSmith for real-time execution tracing, enabling performance bottleneck identification at the node level.

Security & Performance

A deliberate prompt injection test was embedded in the dataset (shipment record 10) to validate input sanitization. Latency profiling revealed that resolution and critic nodes are the primary performance bottlenecks; chunking optimization and retry reduction improved throughput. GPU acceleration is recommended for production-scale deployments.

Key Findings

Multi-agentic systems are powerful for automated exception resolution but require human-in-the-loop for VIP/Premium cases
Prompt injection test passed — basic security controls are functional
Resolution and critic nodes account for the highest latency in the execution pipeline
Data quality and preprocessing remain the primary bottleneck for production readiness

Business Recommendations

Expand security testing with adversarial prompt injection probes before production deployment
Migrate to GPU-accelerated compute for faster inference on high-volume shipment queues
Implement priority routing to ensure VIP/Premium cases receive immediate human review
Invest in data quality and preprocessing pipelines to improve agent decision accuracy

LangGraph LangChain ChromaDB LangSmith Sentence Transformers PyMuPDF Pandas / NumPy

Full Project List →

Education & Credentials

UTA

University of Texas at Austin

Post-Graduate Certificate — AI Agents for Business Applications

Graduate-level certificate program focused on applied artificial intelligence, multi-agent systems, LLM engineering, and enterprise AI deployment. Projects span healthcare informatics, financial analysis, and logistics automation. GPA 3.78. Expected July 2026.

AI Agent Architecture LangGraph & LangChain RAG Systems Business Intelligence Database Engineering

UOP

University of Phoenix

Master of Science — Computer Information Systems

Graduate-level program focused on programming, database systems, networks, and project management. GPA 3.82.

Programming Computer Networks Project Management Database Engineering

KSU

Kennesaw State University

Bachelor of Science — Information Systems

Bachelor's program focused on information sciences. GPA 3.72.

C++, Java Object Oriented Analysis and Design RDBMS Oracle Operating Systems

Contact Jason