From applied research to production AI.
HADI AI MLOPS SOLUTIONS is the consulting practice of Hadirou Tamdamba — an AI & MLOps engineer who builds machine learning and generative AI systems that survive contact with production.
The practice sits deliberately at the intersection of research and engineering. A background in Applied Mathematics & Statistics — and two peer-reviewed publications — brings scientific rigor to how models are evaluated. Four-plus years of hands-on delivery across Luxembourg, Switzerland, France and Burkina Faso bring the engineering discipline to get them deployed, governed and maintained.
The result is a partner who is equally comfortable choosing an evaluation metric and writing the CI/CD pipeline that ships the model behind a secured API. Work spans regulated, high-stakes domains — healthcare, finance and public health — where an AI system has to be not just accurate, but reliable, explainable and defensible.
The mission is simple: help organizations design, deploy and scale production-ready AI with engineering excellence — turning complex data into measurable, trustworthy outcomes.
Research-grade rigor
Every model is evaluated honestly, with metrics chosen for the problem — not for the headline. Rooted in applied mathematics and peer-reviewed research.
Production is the goal
A model in a notebook is not a deliverable. The finish line is a reproducible, tested, deployed system a team can operate and trust.
Governance by design
In healthcare, finance and insurance, AI must be explainable, monitored and compliant. Governance is built into the architecture, not bolted on.
Business value over buzzwords
The work is judged by outcomes and the confidence it inspires — never by the length of a technology list.
Machine Learning & Deep Learning
Generative AI & LLMs
MLOps & Cloud
Data & Engineering
APIs & Applications
Governance & Foundations
2025 — 2026
AI × MLOps Engineer
HALE-X · Luxembourg · Hybrid
Designing and deploying scalable AI within robust MLOps pipelines for a company using AI and digital-twin technology to turn complex health data into clinical insight.
- Built end-to-end MLOps pipelines for AI-driven healthcare applications
- Developed ingestion and preprocessing across multi-source medical data
- Trained, fine-tuned and evaluated predictive and generative models for clinical use cases
- Worked with clinicians and cross-functional teams on compliance, scalability and performance
2024 — 2025
AI Trainer · Prompt Engineer
DataAnnotation · Remote (USA)
Engineered and evaluated prompts and AI-generated code across data analytics, data science and statistics use cases.
- Designed specialized prompts to make model outputs relevant and accurate
- Reviewed, tested and debugged AI-generated code to maintain quality
- Improved overall workflow efficiency by 25% through process automation
2025
Machine Learning / MLOps Engineer
Freelance · Self-Employed · Geneva, Switzerland · Hybrid
Built a production-ready credit-risk system for a financial institution — from EDA to a serverless API on AWS with full CI/CD.
- Full-stack ML pipeline: EDA → feature selection → SMOTE → optimization
- Containerized the model with Docker + FastAPI and automated tests
- Deployed a serverless API on AWS Lambda behind a custom domain
- Built CI/CD with Docker and GitHub Actions
2024
Data Scientist (Trainee)
Luxembourg Institute of Health · Strassen, Luxembourg
Estimated influenza cases from wastewater viral loads on the VIRALERT project, in collaboration with the Luxembourg Institute of Science and Technology.
- Benchmarked 12 predictive models on four years of wastewater data
- Authored a Statistical Analysis Plan to guide the pipeline
- Contributed to associated scientific publications
2020 — 2022
Data Analyst · Statistician
Centre MURAZ, Institut National de Santé Publique · Bobo-Dioulasso, Burkina Faso
Statistical analysis and scientific writing across public-health research programmes, including COVID-19 and reproductive-health studies.
- Co-authored a peer-reviewed study on pesticide exposure and infertility
- Supported the ANRS COV-13 EMuL-COVID-19 clinical research project
- Contributed to the POCAO West-Africa key-populations programme
Master's Degree — Applied Mathematics & Statistics (Data Science)
Université Rennes 2 · L'Institut Agro Rennes-Angers
Rennes, France
Co-accredited by Université de Rennes, Institut Agro, INSA and ENSAI. Statistical learning, machine/deep learning, time series, Bayesian modelling.
Bachelor's Degree — Applied Mathematics: Statistics & Computer Science
Université Nazi Boni
Bobo-Dioulasso, Burkina Faso
Have an AI system that needs to reach production?
Let's talk about the problem you're trying to solve — and whether AI is the right tool for it. No buzzwords, just an honest technical read.