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 (SageMaker, 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. 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, GitLab CI, or GitHub Actions. Strong knowledge of AWS services and infrastructure‑as‑code using Terraform or CloudFormation. Experience deploying AI/ML models in secure, regulated environments and implementing monitoring/observability solutions. #J-18808-Ljbffr