What type of AI engineering projects do you specialize in?

I specialize in production-grade LLM applications: multi-agent autonomous systems, Retrieval-Augmented Generation (RAG) pipelines, LLM evaluation frameworks (like GREAT), MCP server integrations, and AI product engineering.

Are you available for full-time roles, contract work, or advisory?

Yes! I am open to full-time AI engineering positions, contract consultations, and high-impact advisory projects across US, UK, and EU time zones.

How do you ensure AI system reliability and prevent hallucinations in production?

I implement rigorous multi-agent evaluation benchmarks, deterministic fallback guardrails (such as LoopGuard circuit-breakers), vector-search grounding, and automated regression testing prior to deployment.

What is your guaranteed response time for inquiries?

I guarantee a response within 24 hours for all professional inquiries sent via email or LinkedIn (typically responding in less than 4 hours).

How do you handle data privacy and security compliance?

All AI solutions adhere to strict data privacy principles (GDPR compliance, zero data retention policies on sensitive APIs, private VPC vector stores, and robust access controls).

Charbel Toumieh — AI Engineer | Agentic Systems & RAG Architect

Open to opportunities · < 24h response

> whoami

AI Engineer — agentic systems · RAG · LLM evaluation — shipping intelligence to production.

3+yrs building AI 24public projects −90%QA effort automated 34+agents scored by GREAT

Start a Project / Hire Me Explore Works Download résumé

Agentic Systems◆Multi-Agent Orchestration◆RAG Architecture◆MCP Servers◆LLM Evaluation◆Prompt Architecture◆Vector Search◆Workflow Automation◆ Agentic Systems◆Multi-Agent Orchestration◆RAG Architecture◆MCP Servers◆LLM Evaluation◆Prompt Architecture◆Vector Search◆Workflow Automation◆

LangGraph◇LangChain◇Structured Outputs◇Tool Calling◇Self-Verification◇Hallucination Reduction◇PyTorch◇Semantic Search◇ LangGraph◇LangChain◇Structured Outputs◇Tool Calling◇Self-Verification◇Hallucination Reduction◇PyTorch◇Semantic Search◇

[ 01 ]

Profile

// about

I build production-grade LLM systems — agents, RAG pipelines and evaluation frameworks that leave the notebook and survive real users.

I hold an MSc in Applied Artificial Intelligence from the University of Warwick and lead AI engineering at Gabriel & Co. in New York — where I designed GREAT, an AI quality-assurance platform that replaced manual call review across 34+ agents and three channels.

My work covers the full arc of an AI product: discovery, prompt and agent architecture, MCP servers and tool integration, evaluation and A/B testing, and the dashboards that make it legible to the business. Before that — data products serving 100K+ users at CMA CGM, and Meta-funded multimodal research on paediatric ECG models.

practiceagents · RAG · evals · automation
nowAI & LLM Engineer @ Gabriel & Co.
educationMSc Applied AI @ University of Warwick
timezoneUK-based · overlaps US & EU hours

[ 02 ] — flagship system · scroll to zoom

great — quality-assurance platform LIVE

$ great report --scope=all
┌─ scope      34+ agents · 3 channels (inbound / outbound / email)
├─ replaces   manual call review
├─ qa effort  −90%
├─ eval quality +65% via structured outputs & A/B prompt testing
├─ rep scores 80s → 90s
└─ integrates 24+ internal tools · AWS data · dashboards
$ great stack
agents · MCP servers · RAG pipelines · evaluation frameworks · n8n · Node.js · PHP
$

Designed & led end-to-end @ Gabriel & Co., New York 2024 — present

[ 03 ]

Experience

// 2023 — present

01 Jun 2024 — Present New York, US · remote

AI & LLM Engineer

Gabriel & Co.

−90% QA effort. Led the development and evolution of GREAT — automated scoring across 34+ agents and three channels, lifting rep quality scores from the 80s into the 90s.
+65% evaluation quality. Designed evaluation frameworks, scoring methodologies and prompt architectures assessing customer interactions at scale.
24+ tools integrated. Architected AI agents, MCP servers, RAG pipelines and workflow automation across internal tools, AWS data sources and dashboards.
Shipped AI for recommendations, retailer-brand monitoring, content generation and operational analytics.

llm-evaluation · prompt-architecture · mcp · rag · node.js · php · n8n

02 Jan 2026 — Jun 2026 Remote

Artificial Intelligence Researcher

Chehab Lab

6+ encoders benchmarked. Researched Semantic Slice Selection for 3D medical imaging — BioMedCLIP, DINOv3, DenseNet, ResNet, MedVAE, RAD-DINO.
Designed experiments with cosine similarity, L2 distance, semantic-preservation and compression metrics to quantify representation collapse in medical AI models.

medical-imaging · vision-encoders · representation-learning

03 Jan 2025 — Oct 2025 Marseille, FR · remote

Data Strategy & Engineering

CMA CGM

+33% targeting accuracy. Enhanced clustering models for customer segmentation and route analysis.
100K+ users served. Owned roadmap and delivery of two core data products across 100+ shipping routes and financial datasets.
−40% reporting time. Engineered ETL pipelines and automated dashboards covering 50+ KPIs.

data-products · clustering · etl · kpi-dashboards

04 Jan 2024 — Jan 2025 California, US · remote

AI & LLM Researcher

Meta-Funded Research Collaboration

Advanced a multimodal ECG model in a 7-researcher Meta-funded project — high-accuracy reconstruction and atrial-fibrillation prediction for paediatric care.
+20% reading accuracy. Improved AI-assisted ECG interpretation via evaluation, behavioural analysis, multimodal prompt experimentation and benchmarking.

multimodal-ai · healthcare · benchmarking

05 Nov 2023 — Feb 2024 Baabda, LB

Software Engineering Intern

BMB Group × PepsiCo

−30% package size. Built a .NET MAUI Blazor app with PepsiCo, adding offline Android SQLite support for stronger reliability and security.

.net-maui · blazor · sqlite · android

06 Jul 2023 — Oct 2023 Naqqache, LB

AI Engineering Intern

Intalio

97% extraction accuracy. Engineered a Flask OCR API handling 100+ daily requests with YOLOv8, OpenCV and Tesseract — efficiency +35% via pipeline optimisation.

yolov8 · opencv · tesseract · flask

07 Mar 2023 — Jun 2023 Dbayeh, LB

Software Engineering Intern

eMcREY

−40% processing time. Created XML, Java and MySQL applications on Oracle platforms in a 4-person team, automating three core business processes.

java · mysql · oracle · xml

[ 04 ]

The Stack

// capabilities

hover / tap the core to decompose

AI Agents

RAG & Retrieval

LLM Evaluation

MCP & Tooling

Computer Vision

AI Product

ai / llm: AI AgentsMulti-Agent SystemsAgent OrchestrationRAGVector DatabasesSemantic SearchPrompt EngineeringStructured OutputsLLM EvaluationA/B Prompt TestingHallucination ReductionTool / Function CallingSelf-VerificationMCPLangChainLangGraphLlamaIndex
ml & data: Clustering & SegmentationModel EvaluationBenchmarkingExperimental ValidationSQL / SQLiteETL Pipelines
backend & apis: PythonFlaskNode.jsPHPJavaREST APIsAPI Integrationn8n
cloud & vision: AWSAzurePyTorchYOLOv8OpenCVTesseract OCREfficientNetGrad-CAMObject Detection
product: Product DiscoveryStakeholder ManagementKPI DefinitionExperimentationRoadmap Prioritisation
languages: English — full professionalFrench — full professionalArabic — native

[ 05 ]

Selected Works

// drag ⟷ scroll · 01 / 06

PROJ.01 — flagshipPython library

LoopGuard

AI-agent reliability framework

Framework-agnostic, zero-dependency loop detector for AI agents — semantic similarity, cycle detection and declarative guardrails for LangChain, LangGraph, CrewAI, Autogen and RAG pipelines.

python · semantic-similarity · guardrailsopen repo ↗

PROJ.02Explainable CV

DermSense

Skin-disease classifier with Grad-CAM

Medical vision pipeline contrasting an SVM/HOG baseline with a PyTorch EfficientNet-B0 transfer model — Grad-CAM heatmaps for diagnostic transparency, served via a responsive web UI.

pytorch · efficientnet-b0 · grad-camopen repo ↗

PROJ.03ML pipeline

OULAD Analytics

Student-retention forecasting, CRISP-DM

Retention analysis and success forecasting — XGBoost with RFECV feature selection, classifiers, regressors and unsupervised clustering (DBSCAN, GMM) behind a Python CLI.

xgboost · dbscan · gmmopen repo ↗

PROJ.04Systems

Commerce Microservices

Containerised FastAPI platform

E-commerce backend split into segregated customer and order services — HAProxy load-balancing, Prometheus scraping and Docker orchestration.

fastapi · docker · haproxy · prometheusopen repo ↗

PROJ.05Full-stack

Network Cell Analyzer

Android telemetry → live dashboard

Native Android diagnostics client capturing LTE/5G/Wi-Fi parameters, streaming telemetry through Flask APIs into a live server-side statistics dashboard.

android-sdk · flask · sqliteopen repo ↗

PROJ.06Algorithms

Supermarket Graph Analysis

Market-basket & path-finding engine

Logistics simulator modelling store layouts as weighted graphs — custom quicksort, DFS/BFS routing and association-rule analytics producing recommendations.

graph-theory · algorithms · testingopen repo ↗

Full archive 18 more

[ 06 ]

Education

// degrees & leadership

conferred

MSc Applied Artificial Intelligence

University of Warwick

Coventry, United Kingdom

conferred

B.E. Computer & Communications Engineering

American University of Beirut

Beirut, Lebanon

AIcademyAI lecturer & curriculum developer, AUB2024 — 2025
IEEEVolunteer — activities & professional development2022 — 2023
PythonDojoLead — daily coding sessions & codebase contributions2021 — 2022

[ 06 ]

Frequently Asked Questions

// inquiries & capabilities

I specialize in production-grade LLM applications: multi-agent autonomous systems, Retrieval-Augmented Generation (RAG) architectures, automated LLM evaluation frameworks (like GREAT), MCP server integrations, and full-stack AI product development.

Yes! I am open to full-time AI engineering positions, targeted contract engagements, and high-impact advisory projects across US, UK, and EU time zones.

I implement multi-layered safeguards: structured output schemas, vector grounding, state-cycle circuit breakers (such as LoopGuard), and automated LLM-as-a-judge regression benchmarks before any deployment.

I guarantee a response within 24 hours for all professional inquiries submitted via email or LinkedIn (typically responding in less than 4 hours during business hours).

All AI architectures strictly comply with GDPR and privacy standards. Client data is handled with zero-data-retention APIs, isolated VPC vector stores, and robust encryption.

[ 07 ]

Endorsements

// recommendations & reviews

"Charbel's architecture of the GREAT platform transformed our customer operations. Automating 90% of our QA review while maintaining rigorous scoring accuracy saved our team hundreds of hours each month."

Engineering Operations Gabriel & Co. · New York

"Charbel demonstrates extraordinary clarity in building agentic systems and RAG architectures. His lectures at AIcademy and technical contributions set a benchmark for practical AI engineering."

Faculty Collaborator American University of Beirut

⚡ Response SLA: Guaranteed under 24 hours (avg < 4h)

> connect --with charbel

Let's ship something
to production.

charbeltoumieh1@gmail.com

Write to me

Open to opportunities Coventry, UK · Beirut, LB US & EU time zones Roles · collaborations · research

GitHub ↗ LinkedIn ↗ Résumé ↓ Privacy Policy ↗ Terms & Conditions ↗

CHARBEL TOUMIEH

AI & LLM Engineer

Artificial Intelligence Researcher

Data Strategy & Engineering

AI & LLM Researcher

Software Engineering Intern

AI Engineering Intern

Software Engineering Intern

LoopGuard

DermSense

OULAD Analytics

Commerce Microservices

Network Cell Analyzer

Supermarket Graph Analysis

Full archive 18 more

MSc Applied Artificial Intelligence

B.E. Computer & Communications Engineering

Let's ship somethingto production.

Let's ship something
to production.