Principal MLOPs Engineer (Canada)

As a Principal MLOps Engineer at Rackspace Technology, you will take charge of architecting and optimizing the ML inference platform while collaborating with cross-functional teams. You'll manage the end-to-end development of high-performance inference systems and provide technical leadership to guide a skilled engineering team. Building scalable models and using optimization techniques will also be key responsibilities.

To qualify for the Principal MLOps Engineer position at Rackspace Technology, you should have a Bachelor's degree in Computer Science with at least 10 years of relevant experience or a Master's degree with a minimum of 8 years. An expertise in Machine Learning, strong knowledge of deep learning frameworks like TensorFlow, and proficiency in Java are critical qualifications for this role.

Key skills for the Principal MLOps Engineer role at Rackspace Technology include a solid understanding of machine learning algorithms, natural language processing, and experience with Apache Hadoop. Moreover, your ability to optimize ML models and proficiency in cloud services, particularly in GCP and Vertex AI, will be essential in this role.

In your role as a Principal MLOps Engineer at Rackspace Technology, you can anticipate a dynamic remote work environment that fosters collaboration. Your communication skills will be vital as you'll work closely with different teams to translate objectives into engineering solutions while mentoring colleagues and encouraging innovative problem-solving.

Rackspace Technology is deeply committed to diversity and inclusion, ensuring equal employment opportunities regardless of various characteristics. As a Principal MLOps Engineer, you will be part of a culture that values unique perspectives, promoting innovation and enabling better service to customers and communities worldwide.

When architecting a scalable ML inference system, I would first assess the specific requirements of the models we plan to deploy. Then, I would leverage distributed computing technologies, ensuring efficient data flow from storage to the compute platform, and consider using orchestration tools to manage model versions and real-time predictions effectively.

I am most proficient in TensorFlow and PyTorch due to their extensive community support and flexibility. TensorFlow provides robust tools for production deployment, while PyTorch’s dynamic computational graph is beneficial for research and development, making them suitable for varied project needs.

To optimize ML models for production, I generally assess their performance metrics and identify bottlenecks. Techniques such as model quantization, distillation, and the use of GPU acceleration help reduce latency and improve efficiency while ensuring robustness in predictions.

I have hands-on experience with the Apache Hadoop ecosystem, including tools like Hive for querying and Pig for data processing. I have utilized Map Reduce for distributed data processing, which has enhanced my ability to work with large datasets effectively in various ML applications.

In a remote team, I prioritize regular check-ins, utilize project management tools, and maintain open lines of communication through video calls and instant messaging. This ensures alignment on project goals and fosters a strong collaborative culture despite physical distances.

To mentor junior engineers effectively, I adopt a hands-on approach, encouraging them to work on real projects while providing guidance. I also stress the importance of feedback, facilitate knowledge-sharing sessions, and encourage them to explore new technologies relevant to their careers.

I have extensive experience with GCP, specifically using Vertex AI for deploying ML models. It allows seamless integration of various GCP services, such as data storage and orchestration, which streamlines the workflow from model training to production.

One significant challenge was addressing the data pipeline's latency and ensuring real-time model predictions. By implementing efficient data streaming practices and optimizing model inference, I was able to improve latency significantly while maintaining accuracy.

To stay updated with ML and AI trends, I regularly read research papers, follow industry leaders on social media, participate in webinars and conferences, and engage with communities on platforms like GitHub and Stack Overflow to learn from peers’ experiences.

My strategies for problem-solving include breaking down complex challenges into manageable components, applying critical thinking to analyze each part, and brainstorming potential solutions. Collaborating with team members can provide additional perspectives that lead to innovative solutions.