Senior SRE - MLOPS - AI Security

Who is Tenable

Tenable is the Exposure Management company. 44000 organizations around the globe rely on Tenable to understand and reduce cyber risk. Our global employees support 65 percent of the Fortune 500 45 percent of the Global 2000 and large government agencies. Come be part of our journey!

What makes Tenable such a great place to work

Ask a member of our team and theyll answer Our people! We work together to build and innovate best-in-class cybersecurity solutions for our customers; all while creating a culture of belonging respect and excellence where we can be our best selves. When youre part of our #OneTenable team you can expect to partner with some of the most talented and passionate people in the industry and have the support and resources you need to do work that truly matters. We deliver results that exceed expectations and we win together!

Tenable AIadds a powerful new layer of visibility context and control to the Tenable One Exposure Management Platform to govern usage enforce policy and control exposure across both theAIthat organizations use and theAIthey build.

Your Role

Were looking for an experienced MLOps / DevOps Engineer to design and manage the infrastructure powering large-scale machine learning systems. Youll be responsible for deploying GPU-heavy models (including LLMs) on cost-efficient production-grade infrastructure supporting both ML workflows and application artifact delivery.

Youll work with cutting-edge technologies like vLLM Triton SageMaker ClearML Karpenter KEDA and EKS ensuring the right balance between performance scalability and cost.

What Youll Do

• Deploy and manage LLMs and deep learning models using vLLM Triton Inference Server and custom API endpoints.

• Build and maintain GPU-aware autoscaling clusters using AWS EKS Karpenter and KEDA optimizing for cost-efficiency and performance.

• Develop CI/CD pipelines using Jenkins and GitHub Actions to automate ML model delivery and application deployments.

• Orchestrate training fine-tuning and inference jobs on AWS SageMaker and ClearML with support for experiment tracking versioning and reproducibility.

• Support backend teams in deploying app artifacts and runtime environments; implement rollback and release strategies.

• Integrate observability tooling (e.g. Prometheus Grafana ELK or OpenTelemetry) for both infrastructure and model performance.

• Collaborate with SREs to enforce high availability disaster recovery and incident response procedures for mission-critical AI services.

What Youll Need

• 6 years of experience in DevOps MLOps or infrastructure roles with a focus on ML model delivery.

• Proven hands-on experience deploying GPU-based models (LLMs vision transformers) using vLLM or Triton.

• Deep knowledge of AWS EKS and Kubernetes with practical experience configuring Karpenter and KEDA for auto-scaling GPU workloads.

• Experience building pipelines using Jenkins GitHub Actions and managing releases for ML and application codebases.

• Familiarity with AWS SageMaker ClearML or similar platforms for ML orchestration and experimentation.

• Strong scripting and automation skills in Python Bash and working knowledge of containerization (Docker).

• Solid grasp of networking IAM and cloud security fundamentals.

Infrastructure-as-code experience using Terraform or Pulumi in production environments.

Were committed to promoting Equal Employment Opportunity (EEO) at Tenable - through all equal employment opportunity laws and regulations at the international federal state and local levels. If you need a reasonable accommodation due to a disability during the application or recruiting process please contact for further assistance.

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