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

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 fo...