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

About the role

Rackner is hiring an MLOps Engineer to move AI/ML systems from prototype to deployment in a secure, mission-focused environment. This role focuses on operationalizing AI/ML capabilities where reliability, performance, and trust are critical.

Responsibilities

• Operate AI/ML systems in secure, real-world environments
• Deploy AI/ML models and ML-enabled applications into these environments
• Migrate workflows from experimentation to containerized, repeatable deployment pipelines
• Support batch and real-time inference architectures
• Bridge model development, software engineering, and platform operations
• Own the entire ML lifecycle, including model versioning, lineage, reproducibility, and lifecycle governance
• Deploy and support Kubernetes-based ML workloads
• Support CI/CD, automation, and repeatable deployment patterns for AI/ML systems
• Monitor model and system performance post-deployment
• Support observability using tools such as Prometheus, Grafana, OpenTelemetry, or similar
• Create runbooks, deployment documentation, and operational playbooks
• Optimize systems for reliability and usability beyond ideal lab conditions
• Deploy and support AI/ML systems in secure, CAC-enabled, or constrained environments
• Support limited compute, restricted data, degraded connectivity, and other operational constraints

Requirements

• U.S. citizenship required
• Active TS/SCI clearance strongly preferred; active Secret may be considered for upgrade

Qualifications

• Core experience in deploying ML systems, AI-enabled applications, or production software
• Strong programming skills in Python
• Familiarity with Docker, containers, or containerized deployment
• Understanding of Kubernetes or cloud-native environments
• Experience with 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

Pay

Industry-leading weekly pay schedule

Schedule

On-site preferred; remote may be considered for highly aligned, clearance-ready candidates able to support secure / CAC-enabled environments and travel as needed