Our story might surprise you. We’re the world’s largest restaurant company—encompassing KFC, Pizza Hut, Taco Bell and Habit Burger & Grill —but there’s a lot more going on behind the scenes than just frying chicken, baking pizzas, and serving up tacos. We put this delicious food in the hands of customers through apps, websites, kiosks, POS, and other digital dining experiences.

We are looking for a skilled AI Support Specialist (MLE Focus) to join our 24/7 operations team, focusing on maintaining and optimizing machine learning pipelines, infrastructure, and deployments. This role involves troubleshooting model deployment issues, ensuring system scalability, and working closely with MLEs to resolve infrastructure-related challenges.

• Bachelor’s degree in Computer Science, Engineering, or a related field.

• 1-2 years of experience in MLE, DevOps, or AI system operations roles.

• Strong knowledge of cloud platforms (AWS, GCP, or Azure) and container orchestration tools (e.g., Kubernetes, Docker).

• Familiarity with MLOps tools and frameworks (e.g., MLflow, Kubeflow, Sage Maker).

• Experience with CI/CD pipelines and infrastructure as code (e.g., Terraform, CloudFormation).

• Excellent problem-solving skills and ability to work in a fast-paced, 24/7 support environment.

Preferred Qualifications:

• Experience with monitoring and alerting tools (e.g., Prometheus, Grafana, Datadog).

• Knowledge of scripting and automation with Python or Bash.

• Understanding of ML model lifecycle management and production best practices.

Operational Support:

• Monitor machine learning pipelines, APIs, and deployment environments for errors and performance degradation.

• Troubleshoot issues related to model inference, deployment failures, or infrastructure bottlenecks.

• Perform root cause analysis for system incidents, documenting findings and implementing preventive measures.

• Support CI/CD workflows for model updates and pipeline changes.

Incident Management:

• Act as the first responder for MLE-related incidents detected via monitoring tools or reported by users.

• Escalate unresolved issues to MLEs or engineering teams and follow through to resolution.

• Track incident metrics (e.g., mean time to resolution) and provide insights for operational improvement.

Collaboration:

• Partner with MLEs to support the deployment of new models and infrastructure changes.

• Collaborate with Data Scientists and AI Engineers to ensure seamless handoffs and alignment on system requirements.

Continuous Improvement:

• Contribute to the development of operational playbooks for model deployments and infrastructure support.

• Identify opportunities to automate repetitive tasks, such as scaling model endpoints or managing resource utilization.