S. citizenship required Build and Deploy Real-World AI Systems Rackner is hiring an MLOps Engineer to move AI/ML systems from prototype → deployment → operational use in a secure, mission-focused environment. This is not a research role—this is where models become reliable, repeatable, auditable systems that run in real-world conditions.

This role is ideal for engineers who want to: Work across AI/ML, Kubernetes, infrastructure, and mission systems Own deployed systems, not just experiments Build high-demand MLOps expertise in secure and constrained environments Deliver technology that is used, trusted, and operational You will help operationalize AI/ML capabilities where reliability, performance, and trust matter most.

What You’Ll Do

Preferred Qualifications

Active TS/SCI clearance Active Secret clearance with eligibility for upgrade Familiarity with ML lifecycle tools such as MLflow, Kubeflow, Airflow, Argo, ClearML, or similar Background in model serving, inference APIs, or deploying ML systems in production Exposure to LLMs, transformer-based models, computer vision, NLP, or applied AI solutions Hands-on work with Kubernetes-based ML workloads Knowledge of observability and monitoring tools such as Prometheus, Grafana, or OpenTelemetry Experience in DoD, defense, intelligence, regulated, or mission-critical settings Work in edge, offline, air-gapped, low-bandwidth, D-DIL, or limited-compute environments Clearance

Requirements

Active TS/SCI clearance strongly preferred Candidates with an active Secret clearance may be considered and supported for upgrade Candidates without an active clearance must be:

MLOps Engineer — AI/ML Systems Deployment (TS/SCI Preferred)