Senior ML Ops Engineer (x/f/m) at Doctolib

Doctolib is seeking a Senior MLOps Engineer to join their ML Platform Team. The engineer will play a crucial role in developing, deploying, and maintaining machine learning models and systems. He/She will collaborate with data scientists, software engineers, and other stakeholders to deliver high-quality solutions that power Doctolib products.

Responsibilities include:

• Collaborating with data scientists and engineers to develop and deploy machine learning models.
• Implementing and maintaining the MLOps pipeline, including version control, continuous integration, and continuous deployment.
• Developing tools, frameworks, and best practices to streamline the model development process.
• Ensuring the availability, reliability, and performance of machine learning models and systems.
• Monitoring the performance of machine learning models in production and identifying areas for improvement.
• Staying up-to-date with the latest advancements in MLOps and machine learning technologies.
• Collaborating with cross-functional teams to gather requirements and provide technical guidance.
• Documenting MLOps processes, standards, and best practices.

Requirements:

• Good team spirit and strong sense of initiative.
• Excellent communication and collaboration skills.
• Bachelor's degree in Computer Science, Engineering, or a related field.
• 5 years of experience as an MLOps Engineer or similar role.
• Proficiency in Python, SQL, Shell Scripting, and Terraform.
• Strong knowledge of machine learning algorithms and concepts.
• Expertise in Deep Learning Frameworks (preferably PyTorch).
• Strong knowledge of cloud platforms like AWS (SageMaker, EC2, ECS, S3, CloudWatch) and/or Azure and GCP equivalents.
• Experience with building MLOps pipelines for containerizing models with Docker.
• Experience with MLOps Frameworks like MLFlow / Kubeflow.
• Proficiency in DevOps practices and CI/CD systems.
• Experience in version control and application packaging tools (Git, Poetry, Docker).
• Familiarity with Apache Spark or other big data processing frameworks.
• Experience with GPU programming (CUDA) and optimization.
• Knowledge of back-end and middleware services (NGINX, FastAPI, or Airflow).

What Doctolib offers:

• Quarterly bonuses and a competitive package
• Continuous training programs on all key competencies
• Transparent internal mobility opportunities
• Mental health and wellbeing offer in partnership with moka.care
• A flexible workplace policy offering both hybrid and office-based mode
• Flexibility days allowing to work in EU countries and the UK 10 days per year