MLSecOps Engineer

Job Overview

Where Machine Learning meets DevOps meets Security. We are looking for a MLSecOps Engineer to build the automated highways that our AI models travel on. You will be responsible for securely automating the training, testing, and deployment lifecycle of our AI systems, ensuring that speed never compromises security.

Key Responsibilities

• Pipeline Automation: Build secure CI/CD pipelines (GitHub Actions, Jenkins, ArgoCD) for automated model training and deployment.
• Supply Chain Security: Implement rigid controls for model artifacts (Model Signing, SBOMs) to prevent supply chain attacks.
• Infrastructure as Code: Manage our global cloud infrastructure using Terraform and Kubernetes (EKS/AKS).
• Compliance Automation: Integrate automated compliance checks (GDPR, PII scanning) directly into the deployment workflow.
• Secret Management: Architect secure systems for managing API keys and identity access (IAM) for our automated agents.

Mandatory Requirements

• DevOps Core: extensive experience with Docker, Kubernetes, and Terraform.
• MLOps: Experience with ML platforms like MLflow, Kubeflow, or Weights & Biases.
• Security Mindset: You assume everything is vulnerable until proven otherwise. Understanding of 'Prompt Injection' at the infra level.
• Cloud Native: Deep expertise in AWS OR Azure ecosystems.

Nice to Have (Bonus)

• Experience with "Confidential Computing" (AWS Nitro Enclaves).
• Certified Kubernetes Administrator (CKA) or Specialist (CKS).
• Experience creating "Model Cards" and documentation systems.

What We Offer

• Autonomy: Ownership of the entire deployment stack.
• Scale: Work with petabyte-scale datasets and massive compute clusters.
• Equity: Significant stock options in a high-growth AI startup.