Applied AI Engineer II

Role Overview

We're looking for an Applied AI Engineer to join our MLOps team and take ownership of the infrastructure that keeps our machine learning models running reliably in production. This role is essential to maintaining the uptime and performance of our ML systems as usage scales. You'll work closely with data scientists, researchers, and software engineers to bridge the gap between experimentation and production—turning research artifacts into robust, monitored, and continuously improving services. This is a hands-on opportunity to shape our on-premises MLOps practices and improve engineering across the ML stack.

Responsibilities

• Collaborate with experienced data scientists and software engineers to gain insights into building scalable and efficient data pipelines, model training, and deployment systems.
• Troubleshoot issues in the entire machine learning infrastructure, from Linux, Docker, and Kubernetes up to the highest levels of our ML stack. Resolve issues, improve system performance, and make our stack the best in the industry.
• Assist in the design and development of on-premises MLOps solutions to support the delivery of machine learning models, and a seamless handover between research and productionization of ML artifacts
• Drive and uphold high engineering standards, bringing consistency to codebases encountered and ensuring software is adequately reviewed, tested, and integrated.
• Optimize existing models for better performance and throughput.
• Incorporate ML model training, validation, and evaluation settings in addition to traditional coding tests like unit and integration testing.
• Build and maintain tools for deployment, monitoring, and operations. - Continuously refine and enhance CI/CD workflows to support the evolving needs of the machine learning infrastructure.

Ideal Candidate

• 3+ years of experience in MLOps or full stack Machine Learning - Good programming skills in a modern programming language (Python, Scientific Python Stack, Cuda).
• Understanding of the MLOps life cycle and experience with MLOps workflows.
• Experience with tools & practices of the trade, such as Kubernetes, GCP/AWS/Azure, CI/CD, common ML frameworks, and data management.
• A keen interest in machine learning engineering and a willingness to explore how it can be scaled effectively.
• Strong desire to learn and good communication skills, with an enthusiasm for collaborative problem-solving.