ML Ops Engineer (m/f/d)

An ML Ops Engineer at Voize will be responsible for designing and managing robust infrastructure that supports the seamless deployment and scaling of machine learning models. This includes optimizing training pipelines, maintaining ML infrastructure, deploying hosted models, and ensuring high performance of data systems. It’s a role that requires close collaboration with our ML and Data teams, enabling the delivery of top-notch AI solutions in healthcare.

To be considered for the ML Ops Engineer role at Voize, candidates should have several years of experience deploying and managing ML models in production. Familiarity with ML frameworks like Triton and data formats such as ONNX is beneficial. Experience with infrastructure as code tools like Terraform and a solid understanding of container orchestration using Kubernetes and Docker are also essential to succeed in this role.

As an ML Ops Engineer at Voize, you will work with a variety of tools and technologies essential for managing ML infrastructure. Expect to utilize frameworks like Triton for model serving, ONNX format for model interoperability, and container orchestration tools like Kubernetes and Docker for deployment. You’ll also engage with infrastructure management tools such as Terraform and Helm to implement best practices in DevOps.

Voize believes in continuous growth and offers various platforms for learning, such as access to resources like Blinkist and Audible. Moreover, we promote an open culture with regular team events and collaboration opportunities to enhance professional and personal development. With our flexible working hours and focus on individual strengths, we aim to foster an environment where everyone can thrive.

The culture at Voize is vibrant and supportive, focusing on communication, feedback, and resilience. As an ML Ops Engineer, you'll be encouraged to see challenges as opportunities for improvement. We value iterative working processes and foster an environment where friendly exchanges of constructive feedback are commonplace, enabling each team member to contribute their ideas and grow together.

In your response, focus on specific projects or experiences you have had deploying ML models. Detail the frameworks you used, any challenges you faced, and how you overcame them. It's also valuable to discuss tools like Kubernetes or Docker, showcasing your understanding of how they contribute to seamless deployments.

Talk about specific monitoring tools and practices you’ve employed to ensure consistent performance of ML infrastructure. Mention any metrics or KPIs you track, how you respond to issues, and the importance of maintaining a reliable system for model training and deployment.

Discuss your familiarity with IaC tools like Terraform. Share specific examples of how you have used IaC to manage infrastructure efficiently, describe the benefits you have experienced, and highlight the importance of scalable and reusable infrastructure in ML Ops.

Emphasize the importance of open communication and collaboration. Provide examples of how you've successfully partnered with data scientists in the past, including how you've incorporated their feedback into your work and how you align your infrastructure with their needs for optimal model performance.

Prepare to discuss a specific, tangible challenge you've faced, ideally one related to infrastructure or model deployment. Explain the scenario, your thought process in addressing the problem, and the resolution. This showcases your problem-solving skills and resilience in a fast-paced environment.

Discuss factors such as performance optimization, resource management, latency reduction, and ensuring robustness in varying data conditions. Highlight your understanding of scalability and reliability in ML Ops, linking these considerations to your previous experiences where feasible.

Talk about the measures you've taken to secure ML deployments, such as access controls, data encryption, and compliance with industry standards. This reflects your awareness of the growing importance of data security in ML infrastructures.

Share insights on your favorite tools for managing containers, such as Kubernetes or Docker Swarm, and explain why you prefer them. Discuss any experiences in orchestrating containers and how these tools help in achieving scalability, reliability, and ease of management.

Highlight the significance of maintaining thorough documentation for ML processes, tools, and systems. Discuss how proper documentation can aid team collaboration and knowledge sharing, especially in environments where speedy iterations and learning are crucial.

Discuss your experience with cloud platforms (e.g., AWS, Google Cloud, Azure) and how you've utilized them for deploying and managing ML models. Mention specific services you've used and how they streamline ML operations and enhance performance.