01
Data Engineering
Design reliable data platforms and scalable pipelines that transform raw data into production-ready assets.
Data PipelinesETL / ELTReal-Time StreamingData WarehousingDistributed Systems
Stack
Apache KafkaAirflowSparkAWS services
Open to opportunitiesFull-time · Freelance · Hybrid / Remote
Vianney Doraboy
Data & AI Engineer
I design and develop reliable Data and AI solutions: data architecture and processing, model training, API development, and production deployment.
Data EngineeringMLMLOpsAI EngineeringLLM/GenAICloud
DATA → AI
AI ENGINEERING
Engineering flowDATA→ML/MLOPS→AI→Production
Python/AWS/Spark/Kafka/AI
Expertise
Building intelligent systems
from data to production.
I combine Data Engineering, AI Engineering, ML/MLOps, and Cloud & Software Engineering to build reliable, integrated, production-ready systems.
02
AI Engineering
Build production-oriented AI systems by integrating LLMs, data, APIs, vector search, and RAG architectures.
Generative AILLM ApplicationsRAG SystemsVector SearchRAG EvaluationAI APIs
Stack
LLMsFastAPIPostgreSQLpgvectorRAGMCP
03
ML & MLOps
Productionize the ML lifecycle, from model training and experiment tracking to deployment, monitoring, and automation.
Machine LearningMLOpsExperiment TrackingModel RegistryModel ServingMonitoringCI/CD
Stack
Scikit-learnMLflowFastAPIPrometheusGrafana
04
Cloud & Software Engineering
Design reliable backend services and cloud architectures to integrate, deploy, and operate Data & AI systems in production.
Cloud ArchitectureBackend EngineeringAPI DesignContainerizationCI/CD
Stack
AWSFastAPIDockerGitHub Actions
Engineering Mindset
From experimentation to production.
01Data → Pipeline
02Model → System
03Prototype → Production
Data Engineering/AI Engineering/ML & MLOps/Cloud & Software Engineering
Selected Projects
Turning ideas into
Data & AI systems.
A selection of Data Engineering, Machine Learning, and AI Engineering projects, ranging from real-time data platforms and RAG systems to MLOps, cloud architectures, and intelligent solutions powered by simulation and optimization.
Real-Time Streaming Data Platform
Designed and developed a real-time data platform built on a distributed event-streaming architecture. Events are collected, validated, and processed continuously. Usable data and aggregations are stored in an S3-compatible data lake. Invalid events are kept separately to support investigation.
Production-grade architecture
Breast Cancer MLOps Platform
An MLOps platform covering data preparation, model training and evaluation, registration in MLflow, API deployment, and operational monitoring. Each prediction identifies the version of the model used. Experiments and service metrics make it easier to track models and monitor API activity.
Production-oriented MLOps
AI-Powered Peacebuilding Knowledge Assistant (RAG)
Multilingual documentary assistant (FR/EN) designed to retrieve information from a complex knowledge corpus and produce source-grounded answers with verifiable citations.
Production-oriented design
04Cloud & Data
Cloud Data Lakehouse
Design of an AWS data lakehouse that brings together data from multiple sources, organizes its transformation, and makes it available for analytics.
Cloud architecture for analytics
Open to collaboration
A Data or AI challenge to tackle?
From Data platforms and cloud architectures to Machine Learning systems, MLOps, and AI applications powered by LLMs and RAG.
Professional Experience
Building Data & AI systems,
from architecture to production.
A career combining data engineering, AI systems, machine learning, cloud, and software development, with an engineering approach focused on building reliable and production-ready solutions.
2020 — PresentFreelance / Independent
Software & AI Engineer
Client work, professional AI project & open-source projectsParis / Remote
Current
- AI Engineering — Peacebuilding Knowledge AssistantJune 2026 -
Professional project: design and development of a multilingual RAG knowledge assistant using Python, FastAPI, PostgreSQL/pgvector, embeddings and LLM APIs, including document ingestion, vector search, SSE streaming, verifiable citations, and evaluation of retrieval, groundedness, citation quality and overall response quality.
- Software Engineering — Client Projects2022 — 2026
Design and development of 6 web applications addressing different business needs, from functional analysis through production delivery, including frontend/backend development, APIs, databases, authentication, external integrations, testing, deployment and maintenance.
- Qardyl — Full-Stack & Real-Time AnalyticsClient project
Design and development of a digital business card application using Next.js/React, TypeScript, Node.js/Fastify, Clerk, Supabase/PostgreSQL and Cloudinary. Development of analytics to track views, interactions and leads, with dashboard reporting and real-time updates through Server-Sent Events (SSE). Use of AWS services throughout the evolution of the project, including Amplify, API Gateway, VPC, DynamoDB, CloudFront, S3 and SES.
- Data Engineering — Open-Source ProjectsIn parallel
Design of batch and streaming data platforms and pipelines using Python/PySpark, Spark Structured Streaming, Kafka, Airflow and AWS, covering ingestion, distributed processing, data validation and quality, Data Lake architectures, Avro/Schema Registry, observability and CI/CD.
- Machine Learning & MLOps — Open-Source ProjectsIn parallel
Development and productionization of ML workflows covering data preparation, training, experimentation, versioning, serving and monitoring using Scikit-learn, MLflow, FastAPI, Docker, PostgreSQL, Prometheus/Grafana, automated testing and CI/CD.
- Business & Solution DesignCross-functional
Analysis of functional and business requirements and translation into appropriate technical solutions, from defining data flows and application features to architecture decisions and integration of the different system components.
PythonFastAPIAWSNextjs / ReactTypeScriptLLMRAGMLOps
2019 — 2020Fixed-term Contract
Data Scientist
EES-CLEMESSYMulhouse, France
Development of Machine Learning and time-series models for industrial use cases, including performance prediction and process-time estimation for vibrating presses.
Analysis and modeling of industrial data to identify factors influencing equipment performance and develop predictive models.
Contribution to data architecture, technology selection, and the development of monitoring and visualization interfaces.
PythonMachine LearningTime SeriesData ScienceStatistics
02 — 08 2019R&D Internship
Data Scientist
EES-CLEMESSYMulhouse, France
Development of a Data Science PoC for the SmartForest platform, focused on analyzing data from industrial systems.
Application of statistical and Machine Learning methods for anomaly detection and forecasting using industrial data.
Exploration of predictive maintenance approaches to enable earlier detection of abnormal behavior and potential equipment failures.
PythonAnomaly DetectionPredictive MaintenanceMachine Learning
2018Academic Projects
Junior Data Scientist
Supervised ProjectsVannes, France
Development of statistical and predictive modeling projects applied to purchase forecasting and inventory management.
Analysis of historical data and event modeling to identify trends and purchasing patterns.
Implementation of forecasting approaches to improve purchase prediction and support inventory optimization.
StatisticsForecastingData AnalysisPredictive Modeling
2017Internship
Data Analyst
Turkish Statistical InstituteIzmir, Türkiye
Statistical analysis and forecasting of demographic data related to migration, births, and mortality.
Contribution to demographic trend analysis and population projection studies.
Participation in the statistical analysis of urban air quality data.
StatisticsForecastingDemographyData Analysis
From data foundations to intelligent systems in production
Data/AI/Cloud/ML / MLOpsEducation
Knowledge built through
data & technology.
An academic foundation in statistics and data science, complemented by continuous learning across software engineering, big data, artificial intelligence, and cloud technologies.
2017 — 2019
Academic Background
Vannes, France
Master's in Data Science & Statistical Modeling
Université Bretagne Sud2017 — 2019
Advanced training in statistics and modeling, machine learning, deep learning, and Big Data, complemented by information systems, databases and data warehousing, Business Intelligence, and complex data processing.
2012 — 2017
Academic Background
Izmir, Türkiye
Bachelor of Science in Statistics
Dokuz Eylül University2012 — 2017
Quantitative training in statistics, probability, and advanced mathematics, complemented by algorithms, programming, databases, operations research, biostatistics, and data analysis.
Cloud Expertise
AWS Skill Builder Knowledge Badges
Knowledge badges covering core AWS concepts, architecture, core networking, serverless and data migration.
AWSServerlessAWS serverless concepts, common architectural patterns, managed services and serverless best practices.View badge
Data / AI / ML & MLOps / Cloud & Software
Always learning · Always buildingTECHNICAL & PROFESSIONAL EXPERTISE
Building intelligent systems, from data to production
A multidisciplinary expertise combining data engineering, AI engineering, machine learning, MLOps, cloud and software engineering.
I combine technical expertise with an end-to-end engineering approach: designing reliable data pipelines, building AI and ML systems, and integrating and deploying them in production environments.
Data Engineering
Apache KafkaApache SparkAirflowPostgreSQLdbt
AI Engineering
OpenAIAnthropicHugging FaceLangGraphMCPpgvector
ML & MLOps
PythonScikit-learnKerasMLflowFastAPIDockerpytest
Cloud & Software Engineering
AWS servicesGitHub ActionsGitTypeScriptReactNode.js
01
Data & AI Architecture
Designing Data & AI architectures, selecting components, integrating systems and bringing them into production.
02
Pipelines & Data Platforms
Batch and streaming ingestion, data transformation, data quality, orchestration and analytics platforms.
03
AI & Intelligent Systems
RAG, LLM systems, semantic search, evaluation and agentic workflows.
04
ML & MLOps Industrialization
Model training, experimentation, versioning, serving, CI/CD, monitoring and machine learning lifecycle management.
Engineering Mindset
From experimentation to production.
01Data → Pipeline
02Model → System
03Prototype → Production
Data/ML/AI/Cloud/MLOps/Software
From data foundations to intelligent systems in production
Contact
Let’s talk about your next Data & AI project.
Have a Data or AI project, an industrialization need, or a technical challenge to solve? Let’s discuss how to design a reliable, scalable, and production-ready solution.
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