Platform engineer, MLOps

As a Platform Engineer, MLOps at Writer, your primary responsibilities include designing and deploying a CI/CD pipeline in collaboration with AI/ML engineers, managing extensive training runs through robust monitoring and logging systems, and ensuring consistent availability of training environments across clusters. You’ll also enhance our containerization and orchestration systems using technologies like Docker and Kubernetes.

To qualify for the Platform Engineer, MLOps position at Writer, candidates should possess professional experience with model training, Huggingface Transformers, PyTorch, and infrastructure as code tools like Terraform. Proficiency in scripting languages such as Python or Bash, expertise in cloud platforms like AWS or Azure, and a solid understanding of monitoring tools such as Prometheus and Grafana are also essential.

In this role, you will be using an array of tools including Docker and Kubernetes for containerization and orchestration, along with monitoring tools like Prometheus and Grafana. Familiarity with frameworks such as PyTorch and Huggingface Transformers, as well as infrastructure as code tools like Terraform, will also be important for success in the role.

The Platform Engineer, MLOps at Writer is integral to our AI initiatives as this role ensures that the infrastructure is not only reliable but also highly efficient in managing AI models. By deploying CI/CD pipelines and overseeing large Kubernetes clusters for GPU workloads, you will enable our AI/ML teams to conduct safe, reproducible experiments, significantly impacting our innovative projects.

At Writer, the work culture for a Platform Engineer, MLOps is dynamic and collaborative. You will be part of a fast-paced team that values innovation and creativity, working alongside smart, hardworking colleagues. The company prioritizes diversity and inclusion, providing an environment where your ideas can thrive and where continuous improvement is embedded in the company's ethos.

A CI/CD pipeline in MLOps is crucial for ensuring that the machine learning models are delivered reliably and efficiently. It automates the process of integrating code changes, testing them, and deploying them to production. A strong answer may include discussing how it improves collaboration, reduces errors, and enables rapid iterations in model development.

When asked about your experience with Docker, focus on specific projects where you've utilized Docker to streamline development and deployment. Discuss how containerization aids in application scalability and reliability, and mention any particular challenges you’ve overcome using Docker to manage complex applications.

For troubleshooting a performance issue in a Kubernetes cluster, describe a systematic approach: start by monitoring resource usage, checking logs, and analyzing metrics. Could you elaborate on tools like Prometheus and Grafana that you would use for monitoring? This will highlight your technical skills and problem-solving ability.

Best practices for optimizing Kubernetes workloads for AI/ML tasks include leveraging GPU resources effectively, using node affinities for deployment, and properly configuring resource limits and requests. Additionally, mention the importance of horizontal pod autoscaling and efficient data management.

To ensure security and reliability in MLOps, you should implement data privacy measures, use role-based access control, and set up comprehensive monitoring and alerting systems. Explain your understanding of secure coding practices and regular vulnerability assessments, emphasizing your commitment to maintaining compliance.

Discuss specific projects you've undertaken on cloud platforms such as AWS, Azure, or Google Cloud. Explain how you leveraged cloud services for ML model deployment, scaling applications, or reducing costs, providing clear examples that demonstrate your expertise.

Provide a concise overview of how you've utilized Huggingface Transformers in building and deploying language models or NLP applications. Mention any specific challenges you faced and how you overcame them, showcasing your expertise in working with modern machine learning frameworks.

When asked about staying updated with trends, detail the resources you use, such as academic papers, industry blogs, workshops, and conferences. Emphasize your commitment to continuous learning and your participation in relevant communities or forums.

Explain your experience with Infrastructure as Code, highlighting tools like Terraform. Discuss how IaC allows for automated environment provisioning, consistency in setups, and documentation of infrastructure as code, ensuring environments are reproducible and auditable.

Monitoring and logging are critical in MLOps for real-time insights into deployed models, identifying performance issues quickly, and ensuring system reliability. Discuss your experience setting up monitoring systems using tools like Prometheus and Grafana and sharing how these tools help in proactive problem-solving.