DevOps Software Engineer

Position Overview

DevOps Software Engineer – Houston, Texas (Onsite) (NASA LTVS Award Contingent). At Intuitive Machines, an innovative and cutting-edge space company, we are making cislunar space accessible to both public and private customers. Our mission is to further science, exploration, communications, and economic progress from the Earth to the Moon and beyond. We are looking for a highly skilled and motivated DevOps Engineer to drive infrastructure automation and CI/CD operations while building a new Monte Carlo simulation cluster and supporting our internal AI initiatives in AWS.

Key Responsibilities

• Design, implement, and maintain infrastructure using Ansible, Terraform, and GitLab CI/CD.
• Build and support a new Monte Carlo simulation cluster for high-performance computing.
• Manage and optimize on-prem Linux systems, emphasizing scalability, performance, and reliability.
• Contribute to the deployment and maintenance of LLM and AI workloads in AWS.
• Develop automation scripts and tools in Python; contribute to C/C++ codebases as needed.
• Support hybrid infrastructure environments that span on-premise and cloud services.
• Collaborate with development teams to enhance DevOps practices and infrastructure as code.
• Participate in Jira-based agile workflows and process improvement initiatives.

Required Qualifications

• Minimum of 5 years of experience in DevOps or infrastructure engineering.
• Hands-on experience with GitLab, CI/CD pipelines, and infrastructure automation.
• Proficient in Python scripting with working knowledge of C and C++.
• Strong experience managing Linux-based environments.
• Expertise with Ansible and Terraform.
• Familiarity with AWS cloud infrastructure and hybrid architectures.
• Exposure to AI/ML environments or experience supporting cloud-based AI systems is a plus.
• Strong problem-solving skills, effective communication, and ability to work collaboratively.
• Must be willing to work fully on-premise.

Preferred Qualifications

• Experience building or supporting HPC environments or simulation clusters.
• Exposure to AI infrastructure such as model orchestration, data pipelines, and inference workflows.
• Familiarity with managing or integrating LLMs in production settings.
• Adaptability and keen interest in learning new tools and technologies.