Python- ML Developer

Key Responsibilities
1. Incident Management & Bug Fixing Investigate and resolve incidents reported on AI algorithms used by L'Oréal researchers (model crashes, wrong outputs, data pipeline failures, performance degradation) Diagnose root causes: data issues, code bugs, dependency conflicts, model drift Apply fixes, test and validate corrections before redeployment Document incidents and resolutions in the ticketing system
2. Algorithm Maintenance Apply patches, dependency updates and environment fixes Ensure models remain functional after infrastructure changes (cloud updates, library upgrades, data schema changes) Maintain and update data preprocessing pipelines feeding the models
3. Code Review & Quality Read, understand and improve existing Python codebases (often written by Data Scientists) Refactor poorly structured code to improve maintainability and reliability
4. Collaboration with R&I Teams
Required Skills
Core Technical Skills — Must Have
Domain Skills Level
Python Advanced Python, debugging, OOP, clean code - Expert
Data manipulation Pandas, NumPy, data wrangling, ETL pipelines- Intermediate
ML Frameworks Scikit-learn, TensorFlow or PyTorch (reading & fixing, not training from scratch) - Intermediate
API & services FastAPI, REST APIs, JSON, basic microservices - Intermediate
Version control Git + GitHub Advanced — see detail below- Intermediate
Environment management Poetry, pip, virtual environments, requirements management- Expert
Cloud — GCP AI Services, GCP Projects, Vertex AI, BigQuery, Service, Account, Google Storage (Buckets), Artifact Registry- Beginner
Containerisation Docker — run, build, debug a container - Intermediate
SQL Ability to query databases and investigate data issues - Intermediate