Austin, TX

We are looking for a highly skilled DevOps Engineer with deep expertise in Kubernetes and extensive experience in MLOps (Machine Learning Operations). If you have a strong background in architecting and managing SaaS applications in Kubernetes, along with proficiency in leveraging AWS services for scalable infrastructure, and are passionate about optimizing systems for machine learning and AI-powered applications, this role offers a fantastic opportunity to make a significant impact.

About Handraise

Founded in 2023, Handraise exists to unlock the power of news for the world’s top brands and beyond. We’re looking for craftspeople who are eager to collaborate with the top Communications professionals in the world to revolutionize how they understand and drive business impact from their news and social media. We're passionate about building a company that solves real problems for Communications professionals, journalists, and eventually broader news audiences. We're all about enabling our team through autonomy and ownership, and we champion a variety of viewpoints. We work hard, have a competitive spunk, and believe in living life beyond work so we can show up the healthiest, happiest versions of ourselves and do our best work for ourselves and our team. We're a tight group of collaborators in Austin, TX, backed by some of the best investors in the world, including Floodgate, Silverton Partners, and Bill Wood Ventures. Our Founder previously was the Founder of PR analytics startup, TrendKite, which was sold to Cision (NYSE:CISN) in 2019 for $225M.

Responsibilities

• Infrastructure Management: Design, implement, and manage scalable cloud infrastructure using AWS services. Ensure high availability and reliability of the infrastructure to support production workloads.

1. Kubernetes Operations: Deploy and manage Kubernetes clusters, ensuring efficient resource utilization and performance optimization. Troubleshoot and resolve issues related to container orchestration and networking.
2. Continuous Integration and Deployment: Develop and maintain CI/CD pipelines to automate the deployment of applications and machine learning models.
3. MLOps Implementation: Collaborate with data scientists and machine learning engineers to operationalize machine learning models. Create and manage data pipelines for training and deploying machine learning models.
4. Monitoring and Performance Optimization: Implement monitoring solutions to track system performance, application health, and user metrics. Analyze system performance and identify areas for optimization and improvement.
5. Security: Ensure best practices for security are followed in the infrastructure and deployment processes.
6. Collaboration and Communication: Work closely with development team to understand their needs and provide DevOps and MLOps solutions.
7. Incident Management: Respond to production incidents and outages, performing root cause analysis and implementing preventive measures.
8. Stay Current: Evaluate and integrate new tools and technologies to enhance the DevOps ecosystem.

Requirements

• Cloud Infrastructure: Expertise scaling infrastructure in AWS.
• Containerization & Orchestration: Experience with Docker and Kubernetes for managing applications and models.
• CI/CD Pipelines: Proficiency in building and managing automated CI/CD pipelines.
• Infrastructure as Code: Experience with Terraform or similar for automating infrastructure deployment.