Principal ML Ops Engineer

Required US Citizens With Security Clearance Only Role: Principal ML Ops Engineer, AI Inference Location: San Francisco, CA ( Remote ) Duration: Long Term Contract We are seeking an experienced ML Ops engineer to lead the architecture and implementation of scalable, production-grade AI inference solutions. You will work closely with our product and research teams to scale SOTA deep learning products and software, focusing on building and releasing high-performance AI runtimes. Responsibilities Architect and manage scalable model training and deployment pipelines for enterprise clients. Lead the strategy for managing and releasing upstream and midstream AI product builds. Design and implement automated testing frameworks to ensure model correctness, responsiveness, and efficiency. Troubleshoot, debug, and upgrade mission-critical Dev & Test pipelines. Define and deploy cybersecurity measures, including continuous vulnerability assessment and risk management for AI systems. Collaborate with cross-functional teams to define market requirements and establish best practices for LLMOps. Stay at the forefront of AI technologies and standards, driving innovation within our practice. Qualifications 5+ years of experience in ML Ops, DevOps, and Automation, with a focus on enterprise software deployment. Expertise with Git, Github Actions, Terraform, Jenkins, Ansible, and modern automation/monitoring technologies. Extensive experience administering Kubernetes/OpenShift in production environments. Deep understanding of Agile development methodologies. Proven experience with at least one major cloud provider: AWS, GCP, Azure, or IBM Cloud. Expert-level Python programming skills. Advanced troubleshooting and systems-thinking skills. Experience contributing to open-source AI/ML projects (e.g., vLLM) is a strong plus. Bachelor’s degree or higher in Computer Science or a related discipline is preferred, but we prioritize practical experience and technical excellence. Skills: agile,jenkins,ops,terraform,gcp,azure,ibm,git,ml,aws,python,cloud