Python/Django Engineer (5-7 years) – AI & MLOps

About the role

You will drive the industrialisation of AI solutions, ensuring they are reliable, scalable and aligned with best technical practices. As a member of the Machine Learning Engineers Chapter you will act as the bridge between Data Scientists and the IT Production team while collaborating with Brussels‑based teams.

Key responsibilities

• Design and implement production‑ready ML pipelines, translating business needs into robust AI services.
• Maintain data sourcing and obsolescence management for AI use cases, ensuring security in Python environments.
• Promote new technologies and testing best practices, continuously proposing automation improvements.
• Develop and optimise Airflow DAGs, integrating with Denodo, PostgreSQL, S3 and ML tools such as MLflow or Kubeflow.
• Ensure high code quality, performance optimisation and effective monitoring of deployed models.

Required profile

• 4+ years of professional Python development experience (OOP, decorators, performance, security).
• Strong English communication skills for daily collaboration with international teams.
• Bachelor’s degree or equivalent; experience in Agile/Scrum environments.

Required skills

• Python (OOP, decorators, code quality, security, performance optimisation).
• Python environment tools: uv, pip, mamba, micromamba.
• MLOps practices: model versioning, deployment, CI/CD (Jenkins, GitLab CI/CD).
• Containerisation and orchestration: Docker, Kubernetes.
• Linux/Cloud administration, Bash scripting.
• Database expertise: PostgreSQL, Denodo Platform, SQL/VQL.
• Workflow orchestration: Apache Airflow DAG development and optimisation.
• Additional knowledge (nice‑to‑have): Django API design, Spark/Hadoop, MLflow, Kubeflow, Kafka, Kibana, ELK, Grafana.

What we offer

• A culture focused on innovation and lasting client value.
• Ongoing learning opportunities to deepen or acquire new expertise.
• A flat, non‑hierarchical structure enabling direct work with senior partners and clients.