software infrastructure engineer

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 Software Infrastructure Engineer to design, build, and maintain the infrastructure and platforms powering our software, data, and ML systems. This is a hands-on engineering role with a strong focus on cloud infrastructure, data platforms, and MLOps. Role Overview This is a hands-on engineering role with a strong focus on cloud infrastructure, data platforms, and MLOps. You will design, build, and maintain the infrastructure and platforms powering our software, data, and ML systems — owning CI/CD, observability, reliability, scalability, and infrastructure automation, while working closely with software, data, and ML engineers to improve development and production workflows. Key Responsibilities Build and evolve infrastructure and internal platforms for software, data, and ML workloads. Design and operate cloud infrastructure across Microsoft Azure and GCP. Build and maintain Kubernetes and container-based environments. Develop infrastructure services, automation, tooling, and APIs. Support large-scale data and image-processing pipelines. Build and improve MLOps infrastructure, including model training, deployment, inference, and lifecycle management. Own CI/CD, observability, reliability, scalability, and infrastructure automation. Work closely with software, data, and ML engineers to improve development and production workflows. Requirements 3+ years of experience in Software Infrastructure, Platform Engineering, DevOps, Backend Infrastructure, or a similar role. Strong software engineering skills, preferably Python. Hands-on experience with Azure and/or GCP. Strong experience with Kubernetes and Docker. Experience with CI/CD and Infrastructure as Code. Experience working with data-intensive or distributed systems. Experience with databases such as PostgreSQL and MongoDB. Strong Linux, networking, debugging, and problem-solving skills. Preferred Experience Experience with MLOps and ML infrastructure. Experience with GPU workloads and Kubernetes-based ML/compute environments. Experience with workflow orchestration such as Argo Workflows. Experience building large-scale data processing pipelines. Experience with Azure ML, Vertex AI, or similar ML platforms. Experience with messaging and distributed systems such as RabbitMQ, Azure Service Bus, or Kafka. Experience with Helm, Terraform, Grafana, Prometheus, and OpenTelemetry. Show more Show less