Adam Regragui

Adam Regragui

Data & Applied AI Engineer

Engineering reliable data systems and AI solutions for real-world impact

Welcome!

Hi, I'm Adam, a data & AI engineer who transforms complex data challenges into elegant, production-ready solutions. From finance to advertising to regulatory tech, I've designed systems that process terabytes of data, power real-time decision-making, and deliver measurable business impact.

This portfolio isn't just a showcase. It's a window into how I approach engineering. You'll find real metrics, interactive visualizations, and the technical insights behind each solution.

Whether you're here to evaluate my work, explore my technical philosophy, or discuss how I can help solve your data challenges, I've built this site to give you a complete picture of what I bring to the table.

Let's dive in!

Experience & Impact

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Polytechnique

Polytechnique Montreal

B.S. Mechanical Engineering

Aug 2018 - May 2023 • Montreal, Canada

Software Engineering Oriented: Focused on building the technical foundation for data systems and computational methods.

Algorithms Distributed Systems Data Structures
Airbus

Airbus

Data Engineer Intern

Jan 2020 - Dec 2020 • Montreal, Canada

Cloud migration & automation: Improved data accessibility and system efficiency for the A220 project.

Restructured operational reporting system to enhance data accuracy and provide actionable insights.

Cloud Migration Automation Reporting
National Bank

National Bank of Canada

Software Engineer

Jan 2022 - Dec 2022 • Montreal, Canada

Improved platform reliability: Added unit/integration test coverage and CI/CD automation.

Built validated data-integration components with observability and schema validation.

CI/CD Data Integration Testing Python
La Presse

La Presse

Data Engineer

Jul 2023 - Nov 2024 • Montreal, Canada

+30% campaign recall: Designed real-time ETL pipelines (10k+ events/hr) and donor-profiling flows.

40% cost reduction: Optimized infrastructure using SQS-driven parallelism and autoscaling.

Real-Time ETL AWS SQS Airflow Python
Regnology

Regnology

Applied AI Engineer

Nov 2024 - Present • Paris, France

62% faster pipelines: Re-architected legacy batch into event-driven streaming (1TB+/week).

+30% RAG accuracy: Improved answer correctness through entity-aware chunking and hybrid retrieval.

Event-Driven RAG LLM AWS Python

Measurable Impact

Real metrics from production systems

Event-Driven Pipeline Transformation

Batch to Real-Time Streaming Architecture

Before 4.0h
After 1.5h
62% Faster

RAG System Optimization

Entity-Aware Chunking & Hybrid Retrieval

0%

Before

0%

After

+30% Accuracy

Real-Time Marketing Intelligence

Event Processing & Donor Profiling System

0% Current
Up from 55% baseline
+30% Improvement

Infrastructure Cost Optimization

Auto-Scaling & Resource Management

Infrastructure Before

60%

Infrastructure After

36%

40% Infra Savings

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My Resume

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Job Match Analysis

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Why Balance Matters

Semantic analysis uses embedding models to capture contextual meaning and relationships between concepts. It recognizes that "data pipelines" and "ETL architecture" are related, even without exact word overlap, identifying transferable skills across different terminology and domains.

Keyword matching uses exact matching to identify specific technical requirements. It ensures critical skills like "Python", "Airflow", or "Snowflake" are explicitly present, catching hard requirements that semantic similarity might underweight.

The balance: Pure semantic matching may overvalue vague conceptual overlap while missing explicit must-haves. Pure keyword matching ignores context and penalizes candidates who use different terminology for the same skills. A 50/50 blend provides the most robust assessment, capturing both hard requirements and deeper skill alignment.
Feel free to adjust the balance between the two as needed.

Match Results

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Skills

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Technical Skills

Soft Skills

Get In Touch

Let's discuss how I can help with your data and AI initiatives