AI DevOps Engineer

Job title: AI DevOps Engineer
Bill rate: ***/hr
Role is onsite
Client address: 2900 W Plano Pkwy Plano, TX 75075
Years of experience required: 9+


An AI DevOps Engineer is responsible for integrating artificial intelligence and machine learning models into operational environments, managing cloud infrastructure, and automating deployment pipelines. This role ensures AI solutions are reliable, scalable, and secure, while collaborating with data scientists, software engineers, and business stakeholders to deliver AI-powered products effectively


Key Responsibilities
AI/ML Deployment and Operations: Deploy, monitor, and maintain AI and ML models in production environments, ensuring performance and reliability Infrastructure Management: Provision, configure, and maintain cloud and on-premises infrastructure, including GPU servers and high-performance computing resources CI/CD Pipeline Development: Build and manage continuous integration and continuous deployment pipelines for AI applications Automation and Scripting: Automate repetitive tasks, infrastructure provisioning, and model deployment using scripting languages like Python Collaboration: Work closely with cross-functional teams, including engineers, data scientists, and product managers, to design and implement AI solutions Monitoring and Security: Implement monitoring, logging, and security best practices to ensure AI systems operate safely and efficiently Documentation and Training: Create technical documentation and provide training to end-users or team members on AI system usage and maintenance


Required Skills and Qualifications
Programming: Proficiency in Python is essential; familiarity with other languages like Bash or Java is beneficial Cloud Platforms: Experience with cloud services such as AWS, Azure, or Google Cloud for AI deployment AI/ML Knowledge: Understanding of machine learning models, data pipelines, and AI frameworks (e.g., TensorFlow, PyTorch) is highly desirable DevOps Tools: Experience with CI/CD tools (Jenkins, GitLab CI), containerization (Docker, Kubernetes), and infrastructure-as-code (Terraform, Ansible) is important Problem-Solving: Ability to troubleshoot complex system issues and optimize AI workflows Communication: Strong collaboration and communication skills to work with technical and non-technical stakeholders