About TeraCyte Analytics

TeraCyte develops advanced imaging and data-processing systems combining microscopy, large-scale image processing, cloud infrastructure, and AI/ML. We are looking for a hands-on MLOps & Cloud Engineer to join our engineering team and help build and maintain the infrastructure powering our ML, data, and production systems.

Role Overview

This is a hands-on role at the intersection of cloud infrastructure and machine learning. You will help build and maintain the infrastructure powering our ML, data, and production systems, working closely with ML, data, and software engineers to bring models and workflows into production.

Key Responsibilities

• Build and maintain cloud infrastructure on Microsoft Azure and GCP.
• Support ML training, deployment, inference, and monitoring workflows.
• Work with Kubernetes and Docker environments, including GPU workloads.
• Build and improve data and ML pipelines.
• Develop infrastructure automation and CI/CD processes.
• Monitor and troubleshoot cloud, ML, and data workloads.
• Work closely with ML, data, and software engineers to bring models and workflows into production.
  
  

Requirements

• 2+ years of experience in MLOps, Cloud/DevOps, Platform Engineering, or a similar role.
• Hands-on experience with Azure and/or GCP.
• Experience with Kubernetes and Docker.
• Experience with monitoring tools such as Grafana, Prometheus, or Azure Monitor.
• Good Python and Linux skills.
• Familiarity with ML workflows, including training and inference.
• Experience with CI/CD, cloud storage, and data pipelines.
• Strong troubleshooting and problem-solving skills.
  
  

Preferred Experience

• Experience with Azure ML, Vertex AI, MLflow, or similar tools.
• Experience with GPU workloads and ML infrastructure.
• Experience with Argo Workflows, Terraform, and Helm.
• Experience with PostgreSQL and MongoDB.
• Experience with large-scale data or image-processing pipelines.
  
  

Ready to apply?

We'd love to hear from you.