McKinsey · Expert Engagement Manager · AI

Building the future
of

I lead end-to-end delivery of large-scale Generative AI solutions for financial institutions — from multi-agent architectures and patent-pending hallucination detection to production agentic systems serving millions.

Let's Talk → View My Work
0×
Faster Promotion
0%
GenAI Adoption Rate
0+
Banks Deployed
0%
Efficiency Gain
// What I Do

Deep expertise across
the AI & cloud stack

🧠

GenAI & Agentic Systems

Designing production multi-agent platforms, RAG architectures, and LLM-powered automation — including a patent-pending hallucination detection framework commercially deployed across banking clients.

AWS Strands LangGraph Bedrock Agents Claude API OpenAI Agents RAG

Deep Dive

  • Patent-pending hallucination detection for LLM and agentic responses — deployed commercially across multiple banking clients
  • Trained a Small Language Model using LoRA fine-tuning, DPO, and knowledge distillation for a Central Asian government tax platform
  • Built a GraphRAG Compliance Engine performing real-time agentic global legal search for a global ride-sharing company
  • Multi-agent orchestration with function calling, vector databases, and prompt engineering at enterprise scale
6+
Production Agent Systems
1
Patent Pending
☁️

Cloud & Infrastructure

Architecting scalable, secure cloud infrastructure across AWS, Azure, and GCP — from serverless microservices to GPU clusters for model training and inference at enterprise scale.

AWS Azure GCP Lambda Labs Docker Kubernetes Terraform

Deep Dive

  • Designed best-in-class AWS architecture for agent orchestration using Strands + Bedrock
  • Deployed SLM on Lambda Lab + Azure hybrid GPU server stack for government production workloads
  • Built full-stack on-premises infrastructure for real-time agentic call center operations
  • MCP / A2A protocol integration, OpenTelemetry observability, and Terraform-based IaC
3
Cloud Platforms
E2E
Infra to Production
🏦

Banking & Risk Automation

Automating RCSA workflows, fraud detection, credit risk modeling, and regulatory compliance — deploying AI-powered risk intelligence across major financial institutions in LATAM and North America.

RCSA Fraud Detection Credit Risk CCAR Compliance

Deep Dive

  • RCSA agentic solution deployed across 6+ regional banks with multi-agent compliance orchestration
  • AI-driven reconciliation algorithms for syndicated lending at a top-3 Japanese bank — 92% backlog reduction
  • Validated fraud, optimization, and credit risk models for multiple LATAM and North American banks
  • Led C&I CCAR modeling and automated validation suites at U.S. Bancorp
92%
Backlog Reduction
16+
Banks Served
⚡

Data Engineering & ML Ops

Building high-performance data pipelines, model training infrastructure, and real-time analytics systems — from Snowflake ETL to LoRA fine-tuning and Monte Carlo scenario analysis.

Python PyTorch Snowflake CUDA CI/CD FastAPI

Deep Dive

  • Automated SQL querying + model training with real-time Monte Carlo & Bayesian scenario analysis for 30k+ employees
  • Developed McKinsey's Model Bias Testing Framework — identified critical biases in 3 production healthcare models
  • Built object detection neural networks and adversarial detection models for DARPA / Department of Defense
  • Researched neural network video compression on IBM HAL cluster at NCSA
30k+
Employees Analyzed
DARPA
Research Partner
// Selected Work

Impact-driven
projects at scale

01

Multi-Agent Workforce Planning Platform

AWS Strands + Bedrock agentic platform enabling automated SQL querying, model training, and real-time Monte Carlo & Bayesian scenario analysis for 30,000+ employees at a major regional bank.

30k+
Employees Covered

Technical Stack

  • AWS Strands agent orchestration
  • Amazon Bedrock (Anthropic Claude)
  • Automated SQL generation & execution
  • Real-time Monte Carlo simulation
  • Bayesian scenario modeling

Impact & Outcomes

  • Covers 30,000+ employees in real-time
  • Automated model training pipeline
  • Best-in-class AWS agent architecture
  • Executive-ready reporting dashboards
02

Real-Time Agentic Call Center Solution

Full-stack from on-premises infrastructure setup through agent development, testing, and production rollout — delivered end-to-end in 6 months.

57%
Efficiency Gain

What Was Built

  • On-premises infrastructure design & setup
  • Real-time agentic response system
  • End-to-end testing & QA framework
  • Production deployment & monitoring

Results

  • 57% efficiency improvement in response handling
  • 6-month concept-to-production timeline
  • Full-stack ownership: infra → agents → deploy
03

RCSA Agentic Automation

AI-powered Risk & Control Self-Assessment solution deployed across 6 different regional banks, automating complex compliance workflows with multi-agent orchestration.

6
Banks Deployed

Architecture

  • Multi-agent compliance orchestration
  • Automated risk assessment workflows
  • Cross-bank deployment framework
  • Regulatory document analysis

Scale

  • 6 regional banks in production
  • Complex compliance workflow automation
  • Standardized RCSA across institutions
04

GraphRAG Compliance Engine

Real-time agentic global legal search engine for a global ride-sharing company, performing automated compliance analysis across international regulatory frameworks.

Global
Legal Coverage

How It Works

  • Knowledge graph of global regulations
  • Agentic retrieval-augmented generation
  • Real-time legal search across jurisdictions
  • Automated compliance gap analysis

Capabilities

  • Multi-jurisdictional regulatory coverage
  • Natural language legal querying
  • Continuous regulatory update ingestion
05

GenAI Knowledge Retrieval — Investment Bank

Led end-to-end development coordinating 10+ cross-functional teams across McKinsey, client, and vendor organizations spanning Europe, India, and the U.S.

85%
Adoption Rate

Leadership Scope

  • 10+ cross-functional technical teams
  • McKinsey, client, and vendor coordination
  • Global delivery: Europe, India, U.S.

Outcomes

  • 85% user adoption rate
  • Enterprise-wide knowledge retrieval
  • Commercial Investment Bank deployment
06

SLM Fine-Tuning — Government Tax Platform

Trained a Small Language Model for a Central Asian government using LoRA fine-tuning, DPO, and knowledge distillation, deployed within an Agentic RAG architecture.

SLM
Custom Model

ML Techniques

  • LoRA fine-tuning for domain adaptation
  • Direct Preference Optimization (DPO)
  • Knowledge distillation from larger models
  • Agentic RAG deployment architecture

Infrastructure

  • Lambda Labs GPU training cluster
  • Azure production serving stack
  • Government-grade security compliance
// Live Demos

See it in action

Demo COBOL → Python Migration
COBOL Demo

AI-powered legacy code transformation — converting enterprise COBOL to modern Python with full logic preservation and test coverage.

Watch on YouTube →
Demo Workforce Management Planning
Workforce Planning Demo

Multi-agent platform leveraging AWS Strands and Bedrock for automated SQL querying, model training, and real-time scenario analysis.

Watch on YouTube →
Demo RCSA Agentic Automation
RCSA Demo

AI-powered Risk & Control Self-Assessment automation — streamlining compliance workflows with multi-agent orchestration.

Watch on YouTube →
// Tech Stack

Tools & technologies
I work with daily

AI & Frameworks
OpenAI Agents SDK
AWS Strands / Bedrock Agents
LangGraph / AutoGen
Claude API / Gemini API
PyTorch / CUDA
Palantir AIP
RAG / Vector DBs
Cloud & Infrastructure
AWS (Lambda, S3, DynamoDB)
Azure / GCP
Lambda Labs (GPU)
Docker / Kubernetes
Terraform / IaC
MCP / A2A Protocols
CI/CD / OpenTelemetry
Languages & Data
Python
C/C++ / CUDA
Julia
JavaScript / TypeScript
SQL / NoSQL
React / FastAPI
Claude Code / CODEX
// Writing

Thoughts on AI,
engineering & strategy

LinkedIn

AI Code Generation Is Barely Touching 30% of Software

Examining the current state and limitations of AI-assisted code generation in enterprise software development.

Read Article → Medium

Revolutionizing KYC with Agentic AI and Semantic Search

Read → Medium

5 Reasons Agentic AI Fails — and How to Avoid Them

Read → Medium

From Art to Engineering: A Practical Rubric for GPT-4.1 Prompt Design

Read → Medium

Enhancing Entity Resolution Using Generative AI — Part 1

Read → Medium

GenAI Defensive Data Poisoning

Read → Medium

Knowledge Graphs vs. Agentic RAG — Part 1

Read → Medium

Reviewing YOLOv4

Read → Medium

YOLOv3 PyTorch Video & Image Model

Read → Medium

What Is ShuffleNet?

Read →
View All on Medium →
// Career

From founder to
enterprise AI leader

2024 – Present
Expert Engagement Manager — AI
McKinsey & Company, Austin, TX
Leading end-to-end delivery of large-scale GenAI solutions for financial institutions. Patent pending on hallucination detection. Promoted 3× faster than standard timeline.
2023 – 2024
Specialist — Data Science & Analytics
McKinsey & Company
Patented hallucination detection framework. Designed AI reconciliation algorithms reducing backlog by 92% at a top-3 Japanese bank. Built McKinsey's Model Bias Testing Framework.
2020 – 2021
Lead Quantitative Risk Model Developer
U.S. Bancorp, Minneapolis, MN
Advanced from intern to Lead Wholesale Portfolio Analyst in 6 months. Led C&I CCAR modeling and automated validation suites, reducing process time by 70%.
2013 – 2020
Founder & CEO
Fast River Logistics Inc., Houston, TX
Founded and scaled an interstate freight trucking company to 48-state operations with consistent profit growth over 6 years.
// About

The person behind
the architecture

Before I ever wrote a line of code for McKinsey, I was running 18-wheelers across 48 states. I founded Fast River Logistics at 22 and spent seven years learning that the hardest engineering problems aren't technical — they're about people, systems, and relentless execution under pressure.

That operator's mindset followed me through a Computer Engineering Master's at Duke, DARPA-funded research in adversarial AI at the Applied Machine Learning Lab, and into McKinsey — where I now lead the end-to-end delivery of enterprise Generative AI solutions for financial institutions worldwide. I've shipped agentic systems serving tens of thousands of users, hold a patent pending on LLM hallucination detection, and was promoted three times faster than the standard timeline.

Fluent in English and Spanish, conversational in Russian, and learning Arabic — I bring a global perspective and a builder's intensity to every system I architect.

Download Résumé
🚀

3× Faster Promotion

Specialist → Engagement Manager in under 1 year at McKinsey. Standard timeline is 3+ years.

📜

Patent Pending

Novel hallucination detection methodology for LLMs and agentic systems, commercially deployed across banking clients.

🎓

Duke + DARPA Research

MS Computer Engineering (3.8 GPA). Developed adversarial detection models for the Department of Defense.

🏗️

Founder at 22

Built Fast River Logistics from zero to 48-state operations with 6 years of consistent profit growth.

🌍

Multilingual

English, Spanish (fluent), Russian (intermediate), Arabic (beginner) — effective across global teams.

// Education

Academic foundations

M.S. Computer Engineering
Duke University
2019–2021 · GPA 3.8/4.0
B.S. Computer Engineering
University of Houston
2017–2019 · GPA 3.9/4.0
B.A. Economics
Grinnell College
2009–2013 · GPA 3.7/4.0
// Let's Connect

Ready to build
something intelligent?

Whether you're exploring AI transformation, scaling agentic systems, or modernizing financial infrastructure — I'd love to hear about your challenge.

Reveriano.Francisco@gmail.com LinkedIn Medium (781) 363-5765