About Pryon:
We’re a team of AI, technology, and language experts whose DNA lives in Alexa, Siri, Watson, and virtually every human language technology product on the market. Now we’re building an industry-leading knowledge management and Retrieval-Augmented Generation (RAG) platform. Our proprietary, cutting-edge natural language processing capabilities transform unstructured data into meaningful experiences that increase productivity with unmatched accuracy and speed.
We are growing our team and adding a DevOps/Infrastructure Engineer to our team focused on platform architecture, CI/CD, and observability infrastructure. In this role, you will own the technical architecture and implementation of our cloud-native, and highly scalable RAG applications. You will design and manage the infrastructure, deployment pipelines, and operational procedures for delivering enterprise-grade AI/ML products.
We’re looking for someone who will drive DevOps best practices and work with engineering teams to implement them effectively. You will own the platform’s reliability, scalability, and operational excellence across multiple cloud environments and on-premises deployments. If you are looking for an opportunity to architect and drive modern DevOps practices for cutting-edge AI/ML products in industry, we would love to hear from you!
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In This Role, You Will:
- Design and implement cloud-native architectures for AI/ML applications using Kubernetes (GKE, EKS, AKS)
- Architect and maintain CI/CD pipelines using modern GitOps practices with tools like FluxCD and BitBucket
- Design and implement observability solutions using Prometheus, Grafana, and other monitoring tools
- Implement operational best practices for scaling RDBMS, OpenSearch, and Minio
- Create and maintain Infrastructure as Code (IaC) using Terraform
- Implement container orchestration strategies using Docker, Kubernetes, and Helm
- Design and implement multi-cloud deployment strategies
- Establish SLOs/SLIs and implement SRE best practices
- Automate operational tasks and create self-healing systems
- Mentor team members on DevOps best practices
- Collaborate with ML engineers to optimize model deployment and serving infrastructure
- Stay current with emerging technologies and best practices in the DevOps/MLOps space
