Job Opportunities API

MLOps Engineer — AI/ML Systems Deployment Location: Dayton, OH preferred Work Arrangement: On-site preferred; remote may be considered for highly aligned, clearance-ready candidates able to support secure / CAC-enabled environments and travel as needed Clearance: Active TS/SCI strongly preferred; active Secret may be considered for upgrade Requirement: U.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 Operationalize AI/ML Systems Deploy AI/ML models and ML-enabled applications into secure, real-world environments Move workflows from experimentation into containerized, repeatable deployment pipelines Support batch and real-time inference architectures Bridge model development, software engineering, and platform operations Own the ML Lifecycle Build and operate production-grade ML pipelines Support model versioning, lineage, reproducibility, and lifecycle governance Work with tools such as MLflow, Kubeflow, Airflow, Argo, ClearML, or similar platforms Build Cloud-Native ML Infrastructure Deploy and support Kubernetes-based ML workloads Containerize models, pipelines, and services using Docker or similar tools Support CI/CD, automation, and repeatable deployment patterns for AI/ML systems Engineer for Reliability Monitor model and system performance after deployment Support observability using tools such as Prometheus, Grafana, OpenTelemetry, or similar Detect and resolve issues related to latency, reliability, drift, degradation, or resource usage Support Secure and Constrained Environments Help deploy AI/ML systems in secure, CAC-enabled, or constrained environments Support limited compute, restricted data, degraded connectivity, and other operational constraints Optimize systems for reliability and usability beyond ideal lab conditions Create Repeatable Systems Develop runbooks, deployment documentation, and operational playbooks Build systems that can be understood, maintained, and operated by others What You Bring Core Experience U.S. citizenship Background in deploying ML systems, AI-enabled applications, or production software Strong programming skills in Python Hands-on work with Docker, containers, or containerized deployment Familiarity with Kubernetes or cloud-native environments Understanding of CI/CD, automation, or pipeline-based delivery Clear communication of technical decisions, tradeoffs, and ownership Ability to operate in a CAC-enabled or secure environment 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 Requ