Senior ML Ops Engineer (x/f/m)

To be considered for the Senior ML Ops Engineer role at Doctolib, candidates typically need a Bachelor's degree in Computer Science, Engineering, or a related field, with an advanced degree preferred. You should have at least 5 years of experience in MLOps, cloud engineering for machine learning, or a similar position. Proficiency in programming languages such as Python, SQL, and shell scripting is essential, alongside a strong understanding of machine learning algorithms and deep learning frameworks like PyTorch.

As a Senior ML Ops Engineer at Doctolib, your primary responsibilities will include collaborating with data scientists to develop and deploy machine learning models, implementing and maintaining MLOps pipelines, monitoring model performance, and ensuring the reliability of ML systems in production. You will also document processes and best practices, and actively contribute to improving the model deployment process.

In the Senior ML Ops Engineer role at Doctolib, you'll be part of a tech-savvy environment that utilizes a variety of technologies. You'll primarily work with Python, AWS services (such as SageMaker and EC2), Docker for container orchestration, and PostgreSQL as a database solution. Familiarity with tools like AWS Kinesis, AWS Lambda, and Terraform for infrastructure as code will also be beneficial.

At Doctolib, the ML Platform Team thrives on collaboration. As a Senior ML Ops Engineer, you'll closely work with cross-functional teams, including data scientists and software engineers, to gather requirements for machine learning solutions. It’s essential to have excellent communication skills and the ability to document processes clearly to ensure everyone is aligned and working towards common goals.

Doctolib offers robust career development opportunities for a Senior ML Ops Engineer through various training programs aimed at enhancing key competencies, such as soft skills and technical expertise. Additionally, there's a transparent internal mobility framework, allowing you to explore other roles within the company. Plus, our dedicated onboarding program ensures you're well-equipped to succeed from day one.

When answering this question, detail each phase of the MLOps pipeline you have built, emphasizing your role in the deployment of machine learning models from development to production. Highlight your experience with CI/CD processes, monitoring, and how you've ensured model performance and reliability. Be specific about the tools, such as AWS SageMaker or Docker, that you leveraged in ensuring efficiency and scalability.

In response, explain your approach to model monitoring, including the metrics you track, such as accuracy, precision, and recall. Discuss the tools you've used for monitoring, like AWS CloudWatch or custom dashboards. Illustrate how you gather data, analyze model performance over time, and how you work proactively with data scientists to iterate on and improve models.

Discuss your familiarity with version control systems such as Git, emphasizing how you manage code and model versioning in MLOps environments. Provide examples of how you've used Git to facilitate collaboration on MLOps projects and ensure consistency in production deployments, including handling branching strategies and managing pull requests.

Articulate your strategies for model scaling, describing how you design adaptable systems that manage increased loads. Mention architectural choices that support scalability, such as using containers with Docker and orchestrating them with Kubernetes, and share any relevant experiences where you successfully implemented these strategies.

Select a specific challenge you've encountered—perhaps related to model drift or performance inconsistencies. Walk through the problem-solving steps you took, showcasing your analytical skills and collaboration with teammates. Highlighting a successful outcome will demonstrate your capability and resourcefulness as an MLOps Engineer.

Share your strategies for keeping your knowledge fresh, including attending webinars, following relevant blogs, participating in community forums, or taking formal courses. Mention any specific technologies or methodologies you have learned about recently and how you plan to integrate them into your work at Doctolib.

Explain your view on the importance of thorough documentation in MLOps processes. Suggest including technical and user documentation that guides team members through workflows, standard operating procedures, and specific configurations. Highlight your experience with tools that help streamline documentation, like Markdown, and the value of maintaining it for knowledge transfer.

Describe how you utilize Docker for containerizing machine learning models. Provide examples of projects where you designed Docker images, set up Docker Compose, or deployed models in a microservices architecture. This will demonstrate both your technical skills and your practical application of Docker in an MLOps context.

Emphasize the importance of collaboration in MLOps, detailing how you interact with data scientists, software engineers, and other stakeholders. Discuss instances that illustrate how effective communication and teamwork led to successful project outcomes, demonstrating that collaboration enhances both productivity and innovation.

When addressing this question, emphasize your belief in open dialogue and constructive criticism. Share a situation where differing opinions arose, highlighting how you facilitated discussions to understand all viewpoints, ultimately leading to a consensus. This response showcases your leadership and communication skills in a collaborative environment.