What youll do

At Doctolib were on a mission to transform the way healthcare is delivered by leveraging the power of AI.

We are seeking a highly skilled motivated and collaborative MLOps Engineer to join our ML Platform Team. The successful candidate will play a pivotal role in developing deploying and maintaining machine learning models and systems ensuring their performance and scalability. You will collaborate with data scientists software engineers platform engineers and other stakeholders to deliver highquality solutions that power Doctolib products for care teams and patients.

Your responsibilities include but are not limited to:

• Collaborate with data scientists and engineers to develop and deploy machine learning models ensuring they meet performance scalability and reliability requirements.
• Implement and maintain the MLOps pipeline including version control continuous integration continuous deployment and monitoring of machine learning models.
• Develop tools frameworks and best practices to streamline the model development and deployment process.
• Ensure the availability reliability and performance of machine learning models and systems proactively addressing any issues that arise.
• Monitor the performance of machine learning models in production identifying areas for improvement and working with data scientists to optimize the models.
• Stay uptodate with the latest advancements in MLOps and machine learning technologies incorporating them into the ML Platform Teams workflows as appropriate.
• Collaborate with crossfunctional teams to gather requirements provide technical guidance and contribute to the development of machine learning solutions.
• Document MLOps processes standards and best practices to ensure knowledge transfer and consistency across the team
• Share advocate your work with the tech community

Our stack

• Programming languages : Python / Pyspark / SQL.
• Cloud providers : AWS / Azure.
• Machine Learning platform : AWS SageMaker
• Container / Orchestration : AWS ECS / Docker
• Data warehouse / storage : AWS S3 / AWS Redshift
• Databases : PostgreSQL
• Search engine : ElasticSearch
• Data capture tool : AWS Kinesis
• ML pipeline : AWS Step Functions / AWS Lambda / AWS SageMaker.
• Infrastructure as code : Terraform

And any other tool you deem relevant!

Who you are

If you dont meet all the requirements below but believe this opportunity matches your expectations and experience we still encourage you to apply!

You could be our next team mate if you:

• Have a good team spirit enjoy learning new skills and have a strong sense of initiative
• Excellent communication and collaboration skills with the ability to work well in crossfunctional teams and write clear documentation.
• Have a Bachelors degree in Computer Science Engineering or a related field; advanced degree preferred
• Have a proven experience 5 years) as an MLOps Engineer Cloud engineer for Machine Learning applications or similar role.
• Are proficient in our core languages : Python / SQL / Shell Scripting / Terraform
• Have a strong knowledge of machine learning algorithms / concepts / trends
• Have an expertise in Deep Learning Framework preferably PyTorch
• Have strong knowledge of cloud platforms like AWS and services like Amazon SageMaker EC2 ECS S3 CloudWatch and/or Azure and GCP equivalents.
• Have experience in building with HuggingFace Technologies including Transformers Diffusers Accelerate PEFT
• Have experience in building MLOps pipelines for containerizing models and solutions with Docker
• Have experience with MLOps Frameworks like MLFlow / Kubeflow
• Are proficient in classic DevOps: strong experience with setting up CI/CD systems with declarative pipelines (GitHub Actions) monitoring devqaprod environments
• Have experience in version control and application/library packaging tools (Git Poetry Docker
• Are familiar with Apache Spark or other big data processing frameworks.
• have experience with GPU programming (CUDA) and optimization for cost and speed
• Have Knowledge of backend and middleware services such as NGINX FastAPI or Airflow

Now it would be fantastic if you:

• Have experience with Kubernetes GitOps tools (e.g. ArgoCD) and/or Kafka
• Have experience with Terraform Enterprise / Hashicorp Vault / Cloudflare is a plus
• Have experience developing in Javascript / Typescript and using deployment in the browser /
• Have experience with ML model quantization and optimization

What we offer

• Quarterly bonuses and a competitive package
• A 6month dedicated onboarding prog