MLOps Engineer

PepsiCo • Albuquerque, NM

1d • Hybrid

About The Position

We are seeking a Mid-Level MLOps Engineer to build, operate, and evolve our Kubeflow-based ML platform on Azure. This role focuses on enabling reliable, scalable, and cost-efficient ML workflows by designing CI/CD pipelines, managing Kubernetes-based ML infrastructure, improving platform observability, and supporting MLE and Data Science teams across the model lifecycle. The ideal candidate is hands-on, comfortable working across infrastructure and ML workflows, and motivated to operationalize best practices in MLOps.

Requirements

• 3–6 years of experience in MLOps, DevOps, or Platform Engineering
• Hands-on experience with Kubeflow, Kubernetes, Terraform (IaC), and containerized ML workloads
• Strong experience with Azure cloud services (AKS, ACR, Storage, Networking, IAM, AD Groups)
• Proficiency in Python and familiarity with ML frameworks (TensorFlow, PyTorch, scikit-learn)
• Experience building CI/CD pipelines (GitHub Actions, Azure DevOps, Argo, etc.)
• Understanding of ML lifecycle management (training, inference, monitoring, retraining)
• Familiarity with observability tools (Prometheus, Grafana, Azure Monitor, DataDog)
• Strong collaboration and communication skills

Responsibilities

• Deploy, configure, and operate Kubeflow components on Azure Kubernetes Service (AKS)
• Support Kubernetes workloads for training, inference, and batch pipelines
• Manage container images, registries, and ML runtime environments
• Assist with Kubeflow and Kubernetes upgrades under senior guidance
• Build and maintain CI/CD pipelines for ML workflows and platform services
• Automate model training, validation, and deployment pipelines
• Implement reproducibility and versioning for data, models, and pipelines
• Implement logging, monitoring, and alerting at the platform level
• Diagnose and resolve workflow, pipeline, and infrastructure failures
• Support SLAs and reliability objectives for ML platforms
• Work closely with MLEs and Data Scientists to onboard workflows onto Kubeflow
• Provide best practices, templates, and documentation for ML teams
• Collaborate with Infra and Security teams on access control and compliance needs
• Assist with collecting and reporting costs at Kubeflow namespace or workflow level
• Identify optimization opportunities related to compute usage and scheduling

Benefits

• Hybrid work model: combination of remote and collaborative office experience to enable innovation
• Entrepreneurial environment in leading international company
• Professional growth possibilities & learning opportunities
• Variety of benefits to support your physical, emotional and financial wellbeing
• Volunteering opportunities to help external communities

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed