Devops Engineer Jobs

About The Position

Requirements

• Bachelor's degree in Computer Science, Data Science, Engineering, Artificial Intelligence, Machine Learning, Mathematics, Statistics, or a related quantitative field, or four years of relevant experience.
• Three years of progressive experience in DevOps within a cloud deployment in AI/ML Ops, data engineering, or related software engineering roles, including at least one year deploying and operating AI/ML production workloads.
• Hands-on experience with cloud-native AI/ML services and infrastructure, preferably Google Cloud Platform (GCP), including services for training, inference, orchestration, and scalable compute.
• Proficiency in MLOps, DevOps, and software engineering practices such as CI/CD, Git-based workflows, containerization, Infrastructure as Code, and automated testing.
• Proficiency establishing connections via API calls and MCP servers.
• Strong SQL and data engineering skills, including experience with large-scale data processing, backend engineering, and integration across enterprise data platforms.
• Experience with API development and systems integration to embed AI/ML capabilities into enterprise applications and workflows.
• Strong analytical and problem-solving skills to diagnose issues across data, models, infrastructure, and orchestration layers.
• Excellent communication and collaboration skills, including the ability to document and explain technical concepts to technical and non-technical stakeholders.
• Must be located in Arizona.

Responsibilities

• Design and implement environments for scalable AI/ML solutions, including predictive models, large language model use cases, and agentic workflows.
• Develop and maintain end-to-end machine learning and RAG pipelines for data ingestion, application feature engineering, training, evaluation, deployment, and lifecycle management.
• Architect and support production-grade DevOps practices, including CI/CD, model versioning, automated testing, monitoring, alerting, retraining, and rollback strategies.
• Deploy and manage AI/ML applications in cloud environments, ensuring they are secure, reliable, performant, and operationally supportable.
• Implement observability and performance evaluation practices to manage compute utilization, performance, and cost efficiency, and work with the AI development team on continuous improvement.
• Ensure AI/ML and agentic solutions comply with data governance, privacy, security, and responsible AI expectations, including HIPAA-aligned practices where applicable.
• Create and maintain technical documentation for deployments, workflows, operational procedures, assumptions, limitations, and support processes.
• Troubleshoot pipeline, infrastructure, model, and integration issues; implement fixes and improve operational stability and delivery efficiency.
• Perform other duties as assigned.