Software Engineer, MLOps - job 1 of 2

As a Software Engineer, MLOps at Motive, your responsibilities will include designing, developing, and deploying MLOps infrastructure to enhance the development and deployment of AI models. You will work on building scalable cloud AI/ML pipelines, automate redundant tasks, and ensure the reliability and availability of production features while collaborating with cross-functional teams.

To qualify for the Software Engineer, MLOps position at Motive, you should hold a Bachelor’s Degree in Computer Science or a related field. Ideally, you will have 1-3+ years of experience in creating scalable software systems, practical knowledge of MLOps tools (such as Docker and Kubernetes), and a solid foundation in computer science concepts.

In your role as a Software Engineer, MLOps at Motive, you will engage with cutting-edge tools and technologies such as Docker, Kubernetes, AWS, Kubeflow, Apache Airflow, and Spark, to design and implement scalable MLOps processes, ultimately streamlining AI model development and deployment.

Your work as a Software Engineer, MLOps at Motive is pivotal in advancing our mission to empower teams in physical operations. By streamlining AI/ML pipelines, you'll enhance the efficiency and insight across various industries, thereby promoting safer and more productive operational practices and directly benefiting our extensive client base.

Motive fosters a dynamic and inclusive work culture where collaboration and innovation are at the core of our values. As a Software Engineer, MLOps, you will be part of a diverse team committed to professional growth and mentoring, creating an environment where every voice is heard and contributions are valued.

Be prepared to discuss specific tools you've used, like Docker or Kubernetes, and describe how you've applied these tools in previous projects to streamline ML operations or enhance model deployment.

Consider discussing specific challenges such as scaling issues or availability problems, and explain the steps you took to resolve them, showcasing your problem-solving abilities and technical expertise.

Explain your approach to CI/CD practices, including automated testing and monitoring strategies you've implemented to ensure that models are effectively evaluated and deployed without disrupting ongoing operations.

Discuss your methods for prioritization, such as assessing project impact, deadlines, and resource availability, emphasizing the tools and techniques you use to stay organized and efficient.

Share specific resources such as blogs, webinars, or conferences you engage with to continually enhance your knowledge in MLOps and how you implement new methodologies into your work.

Illustrate a specific project where you worked closely with teams like Back-end or Data Science. Emphasize your communication strategies and how collaboration led to successful outcomes.

Detail your mentoring style, mentioning specific instances where you trained or guided junior engineers, the methods you used, and the impact it had on their development and the team's overall productivity.

Describe a systematic approach to identifying and troubleshooting performance bottlenecks, focusing on specific metrics you track and tools you use to monitor system reliability.

Discuss the best practices you adhere to for model testing, such as cross-validation, testing with diverse data sets, and how you ensure that models meet expected outcomes before deployment.

Share a project example, detailing the objectives, the technologies used, your role, and the measurable impact it had on the organization or the product, showcasing your project management capabilities.