MLOps Engineer - AI​/ML Systems Deployment - Rackner

Key Responsibilities

• Design, implement, and maintain end‑to‑end CI/CD pipelines that automate model training, validation, and deployment in a secure, TS/SCI‑compatible environment.
• Containerize machine‑learning workloads using Docker and orchestrate them with Kubernetes for scalable, fault‑tolerant production.
• Integrate AI/ML services with AWS (Sage Maker, EKS, S3, IAM) and manage infrastructure as code with Terraform.
• Establish monitoring, logging, and alerting frameworks to ensure model performance, reliability, and auditability.
• Collaborate with data scientists and security teams to enforce compliance, version control, and reproducibility of models.
• Provide on‑call support and troubleshoot production issues, including secure network and CAC‑enabled access requirements.

• Active TS/SCI clearance (or Secret with upgrade potential) and U.S. citizenship.
• 3+ years of hands‑on experience in MLOps, including Python scripting for model pipelines.
• Proficiency with Docker, Kubernetes, and CI/CD tools such as Jenkins, Git Lab CI, or Git Hub Actions.
• Strong knowledge of AWS services and infrastructure‑as‑code using Terraform or Cloud Formation.
• Experience deploying AI/ML models in secure, regulated environments and implementing monitoring/observability solutions.