diff --git a/.env.example b/.env.example
index 54dc3d8..b754fe9 100644
--- a/.env.example
+++ b/.env.example
@@ -17,3 +17,18 @@ APP_LOG_LEVEL=INFO
 APP_SECRET_KEY=change_me
 APP_CORS_ORIGINS=http://localhost:4200
 BACKEND_PORT=8000
+
+# Airflow (webserver + scheduler, LocalExecutor). Base de métadonnées dédiée `airflow` dans le
+# même conteneur `db` (cf. db/init/120-airflow-database.sql), pas un conteneur de plus.
+AIRFLOW_PORT=8080
+# Chiffre les connexions/variables stockées par Airflow. Générer la vôtre :
+# python -c "from cryptography.fernet import Fernet; print(Fernet.generate_key().decode())"
+AIRFLOW_FERNET_KEY=change_me
+# Clé Flask du webserver Airflow (signature de session), distincte de la précédente. Générer la
+# vôtre : python -c "import secrets; print(secrets.token_urlsafe(48))"
+AIRFLOW_WEBSERVER_SECRET_KEY=change_me
+AIRFLOW_ADMIN_USERNAME=admin
+# Compte Airflow créé au premier démarrage (service `airflow-init`), sans rapport avec les
+# comptes `app_user` d'EnerVision.
+AIRFLOW_ADMIN_PASSWORD=change_me
+AIRFLOW_ADMIN_EMAIL=admin@enervision.fr
diff --git a/.gitignore b/.gitignore
index 47574d1..f1715a0 100644
--- a/.gitignore
+++ b/.gitignore
@@ -66,6 +66,9 @@ ml/mlruns/
 ml/mlartifacts/
 ml/mlflow.db
 
+# Airflow : base sqlite locale generee par les tests d'integrite des DAGs (etl/airflow/tests)
+etl/airflow/tests/.airflow_home/
+
 # IDE et OS
 .idea/
 .vscode/
diff --git a/Makefile b/Makefile
index 2a4b3d2..62e6de3 100644
--- a/Makefile
+++ b/Makefile
@@ -1,17 +1,20 @@
 BACKEND := apps/backend
 FRONTEND := apps/frontend
 ML := ml
+AIRFLOW := etl/airflow
 
 .DEFAULT_GOAL := help
-.PHONY: help install install-backend install-frontend install-ml dev dev-backend dev-frontend \
+.PHONY: help install install-backend install-frontend install-ml install-airflow \
+        dev dev-backend dev-frontend \
         lint format typecheck test test-cov test-integration check \
         openapi docker-build db-up db-down db-reset db-logs db-psql migrate bootstrap-admin \
-        ml-lint ml-typecheck ml-test ml-check ml-train ml-score
+        ml-lint ml-typecheck ml-test ml-check ml-train ml-score \
+        airflow-lint airflow-test airflow-check airflow-up airflow-down airflow-logs
 
 help: ## Liste les cibles disponibles
 	@grep -E '^[a-zA-Z_-]+:.*?## .*$$' $(MAKEFILE_LIST) | awk 'BEGIN {FS = ":.*?## "}; {printf "  \033[36m%-16s\033[0m %s\n", $$1, $$2}'
 
-install: install-backend install-frontend install-ml ## Installe les dépendances backend, frontend et ML
+install: install-backend install-frontend install-ml install-airflow ## Installe les dépendances backend, frontend, ML et Airflow
 
 install-backend: ## Installe les dépendances du backend
 	cd $(BACKEND) && uv sync --all-groups
@@ -22,6 +25,9 @@ install-frontend: ## Installe les dépendances du frontend
 install-ml: ## Installe les dépendances du pipeline ML
 	cd $(ML) && uv sync --all-groups
 
+install-airflow: ## Installe les dépendances de lint/test des DAGs Airflow
+	cd $(AIRFLOW) && uv sync --all-groups
+
 dev: ## Lance toute la stack (backend + frontend) en rechargement à chaud
 	@trap 'kill 0' EXIT INT TERM; \
 	$(MAKE) --no-print-directory dev-backend & \
@@ -77,6 +83,24 @@ ml-train: ## Entraine le modele LightGBM. CSV=chemin optionnel, sinon lit ML_DAT
 ml-score: ## Score le prochain pas horaire et l'ecrit dans `prediction`. CSV=chemin optionnel
 	cd $(ML) && uv run python -m enervision_ml.score $(if $(CSV),--csv $(CSV),)
 
+airflow-lint: ## Analyse statique des DAGs Airflow
+	cd $(AIRFLOW) && uv run ruff check .
+
+airflow-test: ## Verifie que les DAGs s'importent sans erreur et ont la structure attendue
+	cd $(AIRFLOW) && uv run pytest
+
+airflow-check: airflow-lint airflow-test ## Chaîne de vérification complète des DAGs Airflow
+
+airflow-up: ## Démarre Airflow (webserver + scheduler, LocalExecutor). db-up requis avant.
+	docker compose up -d airflow-init airflow-webserver airflow-scheduler
+	@echo "airflow  -> http://localhost:$${AIRFLOW_PORT:-8080}"
+
+airflow-down: ## Arrête le webserver et le scheduler Airflow
+	docker compose stop airflow-webserver airflow-scheduler
+
+airflow-logs: ## Suit les journaux du scheduler Airflow (où tournent les tâches, LocalExecutor)
+	docker compose logs -f airflow-scheduler
+
 docker-build: ## Construit l'image du backend
 	docker build -t enervision-backend:local $(BACKEND)
 
diff --git a/db/init/120-airflow-database.sql b/db/init/120-airflow-database.sql
new file mode 100644
index 0000000..05b7f72
--- /dev/null
+++ b/db/init/120-airflow-database.sql
@@ -0,0 +1,5 @@
+-- Base de metadonnees Airflow (webserver + scheduler, LocalExecutor). Separee de la base
+-- applicative : les tables internes d'Airflow (dag_run, task_instance, ...) n'ont rien a faire
+-- dans le schema metier. Meme conteneur Postgres que `enervision`/`enervision_test` plutot qu'un
+-- service dedie, pour ne pas ajouter un conteneur de plus (issue #115).
+CREATE DATABASE airflow;
diff --git a/docker-compose.yml b/docker-compose.yml
index 3f7f9ea..eb79b60 100644
--- a/docker-compose.yml
+++ b/docker-compose.yml
@@ -5,6 +5,32 @@
 
 name: enervision
 
+# Piege : LocalExecutor fait tourner les taches comme sous-processus du scheduler, jamais du
+# webserver. `airflow_ml_state` (modele entraine, magasin MLflow) n'a donc besoin d'etre monte
+# que sur `airflow-scheduler` en pratique, mais reste partage avec le webserver pour que ce
+# dernier puisse au besoin l'inspecter sans en devenir dependant.
+x-airflow-common: &airflow-common
+  build:
+    context: .
+    dockerfile: etl/airflow/Dockerfile
+  environment: &airflow-common-env
+    AIRFLOW__CORE__EXECUTOR: LocalExecutor
+    AIRFLOW__CORE__LOAD_EXAMPLES: "false"
+    AIRFLOW__CORE__FERNET_KEY: ${AIRFLOW_FERNET_KEY:?}
+    AIRFLOW__WEBSERVER__SECRET_KEY: ${AIRFLOW_WEBSERVER_SECRET_KEY:?}
+    AIRFLOW__DATABASE__SQL_ALCHEMY_CONN: postgresql+psycopg2://${POSTGRES_USER}:${POSTGRES_PASSWORD}@db:5432/airflow
+    # Role `enervision_ml` dedie pas encore provisionne (dette assumee, cf. ADR 0003/CLAUDE.md) :
+    # memes identifiants que le backend en attendant.
+    ML_DATABASE_URL: postgresql+psycopg://${POSTGRES_USER}:${POSTGRES_PASSWORD}@db:5432/${POSTGRES_DB}
+    MLFLOW_TRACKING_URI: sqlite:////opt/ml/state/mlflow.db
+  volumes:
+    - ./etl/airflow/dags:/opt/airflow/dags
+    - ./etl/airflow/plugins:/opt/airflow/plugins
+    - ./etl/airflow/include:/opt/airflow/include
+    - airflow_logs:/opt/airflow/logs
+    - airflow_ml_state:/opt/ml/state
+  restart: unless-stopped
+
 services:
   db:
     image: timescale/timescaledb-ha:pg17
@@ -19,6 +45,7 @@ services:
       - pgdata:/home/postgres/pgdata/data
       - ./db/init/100-extensions.sql:/docker-entrypoint-initdb.d/100-extensions.sql:ro
       - ./db/init/110-test-database.sql:/docker-entrypoint-initdb.d/110-test-database.sql:ro
+      - ./db/init/120-airflow-database.sql:/docker-entrypoint-initdb.d/120-airflow-database.sql:ro
     healthcheck:
       test: ["CMD-SHELL", "pg_isready -U $${POSTGRES_USER} -d $${POSTGRES_DB}"]
       interval: 10s
@@ -64,7 +91,54 @@ services:
     ports:
       - "${FRONTEND_PORT:-3000}:80"
     restart: unless-stopped
-        
+
+  # Conteneur unique, jamais redemarre : migre la base de metadonnees puis cree le premier compte
+  # (idempotent, `|| true` sur la creation qui echoue si le compte existe deja). `webserver` et
+  # `scheduler` attendent qu'il se termine avec succes avant de demarrer.
+  airflow-init:
+    <<: *airflow-common
+    restart: "no"
+    command:
+      - bash
+      - -c
+      - |
+        airflow db migrate
+        airflow users create \
+          --username "${AIRFLOW_ADMIN_USERNAME:-admin}" \
+          --password "${AIRFLOW_ADMIN_PASSWORD:?}" \
+          --firstname Admin \
+          --lastname EnerVision \
+          --role Admin \
+          --email "${AIRFLOW_ADMIN_EMAIL:-admin@enervision.fr}" \
+          || true
+
+  airflow-webserver:
+    <<: *airflow-common
+    command: webserver
+    ports:
+      - "${AIRFLOW_PORT:-8080}:8080"
+    depends_on:
+      db:
+        condition: service_healthy
+      airflow-init:
+        condition: service_completed_successfully
+    healthcheck:
+      test: ["CMD", "curl", "--fail", "http://localhost:8080/health"]
+      interval: 30s
+      timeout: 10s
+      retries: 5
+      start_period: 60s
+
+  airflow-scheduler:
+    <<: *airflow-common
+    command: scheduler
+    depends_on:
+      db:
+        condition: service_healthy
+      airflow-init:
+        condition: service_completed_successfully
 
 volumes:
   pgdata:
+  airflow_logs:
+  airflow_ml_state:
diff --git a/docs/architecture/00-vue-ensemble.md b/docs/architecture/00-vue-ensemble.md
index a650a1e..3e99407 100644
--- a/docs/architecture/00-vue-ensemble.md
+++ b/docs/architecture/00-vue-ensemble.md
@@ -57,7 +57,7 @@ flowchart TB
   navigateur --> front
   front -.-> api
   api --> db
-  airflow -.-> db
+  airflow --> db
   prom -.-> api
   grafana -.-> db
   grafana -.-> prom
@@ -67,6 +67,10 @@ Le lien `front -.-> api` reste en pointillé : le frontend appelle bien une API,
 intercepteur répond à sa place tant que les endpoints n'existent pas. Voir
 [30-frontend.md](30-frontend.md).
 
+Le lien `airflow --> db` est maintenant en trait plein : deux DAGs orchestrent l'entraînement et
+le scoring du modèle ML (issue #115), cf. plus bas et [20-backend.md](20-backend.md). Le reste du
+périmètre Airflow envisagé (ingestion, issues #15/#16) reste en pointillé, non construit.
+
 Le lien `prom -.-> api` de même : l'API expose bien `/metrics` au format Prometheus, mais aucun
 collecteur ne vient le lire.
 
@@ -77,15 +81,17 @@ collecteur ne vient le lire.
 | Backend | FastAPI, Python 3.14 | `apps/backend` | `En cours` | Factory, configuration, journalisation, 2 sondes de santé, `/metrics`, contrat OpenAPI versionné, routes `sites`, `alerts`, `recommendations`, `stats/summary`, `readings`, `sensors/status` et `predictions` en lecture (endpoints → services → repositories → models) |
 | Frontend | Angular 22, Node 24 | `apps/frontend` | `En cours` | Tableau de bord sur route `/dashboard`, authentification complète (garde de route, intercepteur de jeton), cinq services HTTP, graphiques Chart.js. `stats`/`alerts` sur fixtures, `predictions` branché sur l'API réelle |
 | Base | PostgreSQL 17 + TimescaleDB | `db` | `Fait` | Bootstrap de l'extension, base de test, chaîne Alembic. Schéma applicatif créé (`site`, `dataset`, `reading` en hypertable, `prediction`, `alert`, `recommendation`) |
-| ML | LightGBM, MLflow | `ml` | `En cours` | Pipeline d'entraînement et de scoring (`enervision_ml.train`/`.score`, features par lags/moyennes glissantes partagées entre les deux, baseline de persistance saisonnière, suivi MLflow local), exposé en lecture via `GET /predictions`. Voir [ADR 0005](../adr/0005-modele-prediction-lightgbm.md) et [ML-START.md](../../ML-START.md). Automatisation (Airflow) et surveillance de dérive (EC06, #44/#45) pas encore construites |
+| ML | LightGBM, MLflow | `ml` | `En cours` | Pipeline d'entraînement et de scoring (`enervision_ml.train`/`.score`, features par lags/moyennes glissantes partagées entre les deux, baseline de persistance saisonnière, suivi MLflow local), exposé en lecture via `GET /predictions`, orchestré par Airflow (`ml_train`/`ml_score`). Voir [ADR 0005](../adr/0005-modele-prediction-lightgbm.md) et [ML-START.md](../../ML-START.md). Surveillance de dérive (EC06, #44/#45) pas encore construite |
 | Infra | Terraform, k3s single-node | `infra/terraform` | `En cours` | Module d'installation du cluster. Jamais appliqué, aucune ressource Kubernetes déclarée |
 | Monitoring | Prometheus, Grafana, Alertmanager | `monitoring` | `Cible` | Rien, hors le `/metrics` exposé par l'API |
-| ETL | Apache Airflow | `etl/airflow` | `Cible` | Rien |
+| ETL | Apache Airflow | `etl/airflow` | `En cours` | Webserver + scheduler (LocalExecutor) tournent via docker-compose, base de métadonnées Postgres dédiée. Deux DAGs (`ml_train` manuel, `ml_score` `@hourly`) orchestrent le pipeline ML existant en sous-processus `uv run` (issue #115). L'ingestion (issues #15/#16) n'a pas encore de DAG |
 | CI/CD | GitHub Actions | `.github/workflows` | `Cible` | Rien |
 
 ## Flux bout en bout
 
-Statut : `Cible`. Aucun maillon de cette chaîne n'existe aujourd'hui, à l'exception de la base.
+Statut : `Cible`. Ce flux d'ingestion (Source → Airflow → hypertable) n'existe pas encore : les
+deux DAGs livrés à ce jour (`ml_train`/`ml_score`, issue #115) orchestrent le pipeline ML, pas
+l'ingestion. Seule la base tourne réellement parmi les maillons ci-dessous.
 
 ```mermaid
 sequenceDiagram
diff --git a/docs/architecture/10-infra.md b/docs/architecture/10-infra.md
index 745c6f5..7bd751c 100644
--- a/docs/architecture/10-infra.md
+++ b/docs/architecture/10-infra.md
@@ -48,6 +48,33 @@ Deux pièges sont documentés en tête du `docker-compose.yml`, ils ne se devine
   l'image, dont `timescaledb-tune`. Ajouter un fichier dans `db/init/` impose donc une ligne dans
   le compose. Voir [`db/README.md`](../../db/README.md).
 
+### Airflow (`ml_train`/`ml_score`, issue #115)
+
+Trois services, `docker compose profiles` non utilisés (démarrage explicite via `make
+airflow-up`, pas dans `make dev`) :
+
+| Service | Rôle | Points notables |
+|---|---|---|
+| `airflow-init` | Migre la base de métadonnées, crée le compte admin | Conteneur jetable (`restart: "no"`), ne redémarre jamais. `webserver`/`scheduler` attendent qu'il se termine avec succès |
+| `airflow-webserver` | UI, port `8080` | `LocalExecutor` : n'exécute aucune tâche lui-même |
+| `airflow-scheduler` | Planifie et **exécute** les tâches (`LocalExecutor`) | Les DAGs y tournent en sous-processus (`uv run --frozen --no-dev python -m enervision_ml...`), c'est lui qui a besoin du volume `airflow_ml_state` |
+
+Construits depuis `etl/airflow/Dockerfile`, contexte `.` (racine du repo, pas `etl/airflow/`) :
+l'image doit pouvoir `COPY` `ml/pyproject.toml`/`ml/uv.lock`/`ml/enervision_ml` pour se
+synchroniser un second environnement Python **3.14** (`/opt/ml/.venv`, `uv sync --locked` à la
+construction), distinct du Python 3.12 qui fait tourner Airflow lui-même. Les DAGs shellent vers
+ce venv plutôt que d'importer LightGBM/MLflow dans le process Airflow.
+
+Piège à connaître : sur un volume `pgdata` déjà peuplé (poste de dev existant plutôt que premier
+`make db-up`), `db/init/120-airflow-database.sql` ne se rejoue pas (PostgreSQL n'exécute
+`docker-entrypoint-initdb.d/` que sur un volume vide). Créer la base `airflow` à la main une fois :
+`docker compose exec db psql -U $POSTGRES_USER -d $POSTGRES_DB -c "CREATE DATABASE airflow;"`.
+
+`libgomp1` est installé explicitement dans l'image (`apt-get`, en root) : l'image Airflow de base
+est minimale et n'embarque pas la runtime OpenMP dont LightGBM a besoin, sans quoi l'erreur
+(`OSError: libgomp.so.1`) n'apparaît qu'à la première tâche réellement exécutée, pas à la
+construction de l'image.
+
 ## Cible de déploiement
 
 Statut : `En cours`. Le module `infra/terraform/modules/k3s/` installe le cluster. Il n'a jamais
@@ -116,6 +143,8 @@ Ces arbitrages sont pris. Ils ne vivaient jusqu'ici que dans des commentaires de
 | SSH du serveur | `22` par défaut | `ssh_port`, redéfinissable |
 | Base applicative | `enervision` | Variable `POSTGRES_DB` |
 | Base de test | `enervision_test` | Créée par `db/init/110-test-database.sql`, nom attendu en dur par `apps/backend/tests/conftest.py` |
+| Base de métadonnées Airflow | `airflow` | Créée par `db/init/120-airflow-database.sql`, même conteneur `db` |
+| Webserver Airflow | `8080` | `make airflow-up`. Scheduler et webserver ne publient que ce port ; les tâches (`LocalExecutor`) tournent côté scheduler, sans port propre |
 
 ## Le trou entre les deux topologies
 
diff --git a/etl/README.md b/etl/README.md
index b835311..324e0a0 100644
--- a/etl/README.md
+++ b/etl/README.md
@@ -344,6 +344,6 @@ uv run ruff check app\etl tests\etl
 
 L'import historique constitue la première brique du pipeline Data EnerVision.
 
-La prochaine étape consiste à orchestrer les traitements ETL avec Apache Airflow, puis à préparer les données nécessaires à l'entraînement du modèle de Machine Learning.
+Airflow tourne désormais réellement (`etl/airflow/`, `make airflow-up`), mais orchestre pour l'instant le pipeline ML (`ml_train`/`ml_score`, issue #115), pas encore ce pipeline ETL : orchestrer `historical_import.py` (normalisation et chargement micro-batch, issues #15/#16) reste à faire.
 
-Airflow sera utilisé comme orchestrateur des traitements existants et ne remplacera pas la logique métier déjà implémentée dans le pipeline ETL.
\ No newline at end of file
+Le principe reste le même que documenté à l'origine : Airflow orchestre les traitements existants sans remplacer leur logique métier, cf. `etl/airflow/dags/ml_train.py`/`ml_score.py` pour un exemple concret de ce patron (des `BashOperator` qui invoquent le script tel quel).
\ No newline at end of file
diff --git a/etl/airflow/Dockerfile b/etl/airflow/Dockerfile
new file mode 100644
index 0000000..52e1877
--- /dev/null
+++ b/etl/airflow/Dockerfile
@@ -0,0 +1,40 @@
+# Image Airflow EnerVision : ajoute le projet ml/ dans son propre environnement Python 3.14,
+# distinct du Python 3.12 qui fait tourner Airflow lui-meme, pour que les DAGs puissent lancer
+# `uv run python -m enervision_ml.train`/`.score` en sous-processus (cf. docs/architecture/
+# 20-backend.md, section Détection d'alertes internes pour le meme raisonnement applique a
+# app/detection). Airflow ne devient jamais un consommateur direct de LightGBM/MLflow.
+FROM apache/airflow:2.10.4-python3.12
+
+# LightGBM est compile contre libgomp (OpenMP), absent de l'image de base (minimale, sans
+# toolchain de compilation). Sans lui : `OSError: libgomp.so.1: cannot open shared object file`
+# au premier `import lightgbm`, seulement au moment ou une tache tourne reellement.
+USER root
+RUN apt-get update \
+    && apt-get install --no-install-recommends -y libgomp1 \
+    && apt-get clean \
+    && rm -rf /var/lib/apt/lists/*
+# Pre-cree, appartenant a `airflow` : docker-compose y monte un volume nomme partage entre
+# `ml_train` et `ml_score` (le modele ecrit par l'un, lu par l'autre). Un volume nomme herite des
+# permissions du repertoire qu'il recouvre a son premier montage ; sans ce chown prealable, il
+# serait cree root:root et illisible par le conteneur, qui tourne en `airflow` (uid 50000).
+RUN mkdir -p /opt/ml/state && chown -R airflow:root /opt/ml
+USER airflow
+
+# L'image de base embarque deja un `uv`, mais trop ancien (0.4.29) pour le format de verrou de
+# `ml/uv.lock`. On le remplace par la version deja pinnee ailleurs dans le depot
+# (apps/backend/Dockerfile).
+COPY --from=ghcr.io/astral-sh/uv:0.11.26 /uv /home/airflow/.local/bin/uv
+
+ENV UV_COMPILE_BYTECODE=1 \
+    UV_LINK_MODE=copy \
+    UV_PROJECT_ENVIRONMENT=/opt/ml/.venv
+
+WORKDIR /opt/ml
+
+COPY --chown=airflow:root ml/pyproject.toml ml/uv.lock ./
+RUN uv sync --locked --no-install-project --no-dev
+
+COPY --chown=airflow:root ml/enervision_ml ./enervision_ml
+RUN uv sync --locked --no-dev
+
+WORKDIR /opt/airflow
diff --git a/etl/airflow/dags/ml_score.py b/etl/airflow/dags/ml_score.py
new file mode 100644
index 0000000..94d7708
--- /dev/null
+++ b/etl/airflow/dags/ml_score.py
@@ -0,0 +1,33 @@
+"""DAG de scoring horaire du modele LightGBM (issue #115).
+
+Planifie toutes les heures, au rythme documente par `enervision_ml.score` (score le prochain pas
+horaire par site). Reutilise le modele ecrit par `ml_train` (DAG separe, declenche a la main) :
+ce DAG ne reentraine jamais rien. Si aucun modele n'a encore ete entraine, la tache echoue
+(`FileNotFoundError`) plutot que de rester silencieuse.
+"""
+
+from __future__ import annotations
+
+from datetime import datetime
+
+from airflow.models.dag import DAG
+from airflow.operators.bash import BashOperator
+
+MODEL_PATH = "/opt/ml/state/models/lightgbm-consumption.txt"
+
+with DAG(
+    dag_id="ml_score",
+    description="Score le prochain pas horaire par site (enervision_ml.score).",
+    schedule="@hourly",
+    start_date=datetime(2026, 1, 1),
+    catchup=False,
+    tags=["ml"],
+) as dag:
+    # `--frozen --no-dev` : cf. `ml_train.py`, meme raisonnement.
+    BashOperator(
+        task_id="score",
+        bash_command=(
+            "cd /opt/ml && uv run --frozen --no-dev python -m enervision_ml.score "
+            f"--model {MODEL_PATH}"
+        ),
+    )
diff --git a/etl/airflow/dags/ml_train.py b/etl/airflow/dags/ml_train.py
new file mode 100644
index 0000000..f4ec36b
--- /dev/null
+++ b/etl/airflow/dags/ml_train.py
@@ -0,0 +1,36 @@
+"""DAG d'entrainement du modele LightGBM (issue #115).
+
+Pas de planification : reentrainer est couteux et sa cadence n'est pas une decision prise
+(cf. `docs/architecture/20-backend.md`). Declenchement manuel depuis l'UI ou la CLI Airflow en
+attendant. `ml_score` (DAG separe, planifie toutes les heures) reutilise le modele que ce DAG
+ecrit, il ne reentraine jamais rien lui-meme.
+"""
+
+from __future__ import annotations
+
+from datetime import datetime
+
+from airflow.models.dag import DAG
+from airflow.operators.bash import BashOperator
+
+MODEL_PATH = "/opt/ml/state/models/lightgbm-consumption.txt"
+MLFLOW_TRACKING_URI = "sqlite:////opt/ml/state/mlflow.db"
+
+with DAG(
+    dag_id="ml_train",
+    description="Entraine le modele LightGBM de prevision de consommation (enervision_ml.train).",
+    schedule=None,
+    start_date=datetime(2026, 1, 1),
+    catchup=False,
+    tags=["ml"],
+) as dag:
+    # `--frozen --no-dev` : l'environnement `/opt/ml/.venv` est fige a la construction de l'image
+    # (groupe `dev` exclu). Sans `--no-dev` ici, `uv run` resynchronise ruff/mypy a chaque
+    # execution : un acces reseau evitable, sur le chemin d'execution d'une tache planifiee.
+    BashOperator(
+        task_id="train",
+        bash_command=(
+            "cd /opt/ml && uv run --frozen --no-dev python -m enervision_ml.train "
+            f"--model-output {MODEL_PATH} --mlflow-tracking-uri {MLFLOW_TRACKING_URI}"
+        ),
+    )
diff --git a/etl/airflow/pyproject.toml b/etl/airflow/pyproject.toml
new file mode 100644
index 0000000..a792f9f
--- /dev/null
+++ b/etl/airflow/pyproject.toml
@@ -0,0 +1,32 @@
+[project]
+name = "enervision-airflow"
+version = "0.1.0"
+description = "DAGs d'orchestration EnerVision (Airflow)"
+requires-python = ">=3.12,<3.13"
+dependencies = [
+    "apache-airflow==2.10.4",
+]
+
+[dependency-groups]
+dev = [
+    "ruff>=0.16.7",
+    "pytest>=9.1.1",
+]
+
+[tool.uv]
+package = false
+
+[tool.ruff]
+line-length = 100
+target-version = "py312"
+src = ["dags", "tests"]
+
+[tool.ruff.lint]
+select = ["E", "W", "F", "I", "N", "UP", "B", "SIM", "RUF"]
+
+[tool.ruff.format]
+quote-style = "double"
+
+[tool.pytest.ini_options]
+testpaths = ["tests"]
+addopts = "-q"
diff --git a/etl/airflow/tests/conftest.py b/etl/airflow/tests/conftest.py
new file mode 100644
index 0000000..0b6e04c
--- /dev/null
+++ b/etl/airflow/tests/conftest.py
@@ -0,0 +1,16 @@
+"""Isole Airflow d'un `~/airflow` reel : `AIRFLOW_HOME` doit etre pose avant le premier `import
+airflow`, donc ici plutot que dans une fixture (les fixtures s'executent trop tard, apres que les
+modules de test aient deja importe `airflow`)."""
+
+import os
+from pathlib import Path
+
+_AIRFLOW_HOME = Path(__file__).resolve().parent / ".airflow_home"
+_AIRFLOW_HOME.mkdir(exist_ok=True)
+
+os.environ.setdefault("AIRFLOW_HOME", str(_AIRFLOW_HOME))
+os.environ.setdefault("AIRFLOW__CORE__LOAD_EXAMPLES", "False")
+os.environ.setdefault("AIRFLOW__CORE__UNIT_TEST_MODE", "True")
+os.environ.setdefault(
+    "AIRFLOW__DATABASE__SQL_ALCHEMY_CONN", f"sqlite:///{_AIRFLOW_HOME / 'airflow.db'}"
+)
diff --git a/etl/airflow/tests/test_dags.py b/etl/airflow/tests/test_dags.py
new file mode 100644
index 0000000..9b03f49
--- /dev/null
+++ b/etl/airflow/tests/test_dags.py
@@ -0,0 +1,52 @@
+"""Tests d'integrite des DAGs : s'importent sans erreur, structure attendue. Pas d'execution
+reelle des taches (ca reclamerait le conteneur avec `uv`/`enervision_ml`), juste la definition."""
+
+from pathlib import Path
+
+import pytest
+from airflow.models.dagbag import DagBag
+
+DAGS_FOLDER = Path(__file__).resolve().parent.parent / "dags"
+
+
+@pytest.fixture(scope="module")
+def dagbag() -> DagBag:
+    return DagBag(dag_folder=str(DAGS_FOLDER), include_examples=False)
+
+
+def test_dags_folder_has_no_import_error(dagbag: DagBag) -> None:
+    assert dagbag.import_errors == {}
+
+
+def test_every_expected_dag_is_discovered(dagbag: DagBag) -> None:
+    assert set(dagbag.dag_ids) == {"ml_train", "ml_score"}
+
+
+def test_ml_train_has_no_schedule() -> None:
+    dagbag = DagBag(dag_folder=str(DAGS_FOLDER), include_examples=False)
+    assert dagbag.dags["ml_train"].timetable.summary == "None"
+
+
+def test_ml_score_runs_every_hour() -> None:
+    # `@hourly` est un alias Airflow pour ce cron, c'est sous cette forme que `.summary` le rend.
+    dagbag = DagBag(dag_folder=str(DAGS_FOLDER), include_examples=False)
+    assert dagbag.dags["ml_score"].timetable.summary == "0 * * * *"
+
+
+def test_ml_train_task_calls_the_training_module(dagbag: DagBag) -> None:
+    tache = dagbag.dags["ml_train"].get_task("train")
+    assert "enervision_ml.train" in tache.bash_command
+
+
+def test_ml_score_task_calls_the_scoring_module(dagbag: DagBag) -> None:
+    tache = dagbag.dags["ml_score"].get_task("score")
+    assert "enervision_ml.score" in tache.bash_command
+
+
+def test_ml_score_reuses_the_model_path_written_by_ml_train(dagbag: DagBag) -> None:
+    entrainement = dagbag.dags["ml_train"].get_task("train").bash_command
+    scoring = dagbag.dags["ml_score"].get_task("score").bash_command
+    chemin_modele = "/opt/ml/state/models/lightgbm-consumption.txt"
+
+    assert chemin_modele in entrainement
+    assert chemin_modele in scoring
diff --git a/etl/airflow/uv.lock b/etl/airflow/uv.lock
new file mode 100644
index 0000000..0fd3a71
--- /dev/null
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