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

MLOps Engineer — AI/ML Systems & Deployment (TS/SCI Preferred)
Dayton, OH (On-site Preferred) | Remote Eligible (U.S.-based, Clearance-Ready)
Clearance-Eligible Role | Mission-Critical AI/ML Systems

About the Role

At Rackner, we build systems where advanced technologies move beyond prototypes and into real-world operational use.

We are seeking an MLOps Engineer to support the deployment and lifecycle management of AI/ML systems within a secure, mission-focused environment.

This is not a research role.

This is where models become reliable, deployable, and auditable systems.

You will operate at the intersection of:

• machine learning
• cloud-native infrastructure
• distributed systems

…and ensure AI/ML systems are production-ready in environments where reliability and performance matter.

What You’ll Do

Own the ML Lifecycle (End-to-End)

• Build and operate production-grade ML pipelines
• Orchestrate workflows using Kubeflow, Airflow, or Argo
• Implement model versioning, lineage, and reproducibility standards

Operationalize AI/ML Systems

• Deploy models into secure and constrained environments
  Transition workflows from experimentation → containerized pipelines → production systems
  Enable both batch and real-time inference architectures

Engineer for Reliability

• Design systems for reproducibility, auditability, and stability
• Monitor model performance and system health using Prometheus, Grafana, OpenTelemetry
• Detect and resolve issues such as model drift and system degradation

Build Cloud-Native ML Infrastructure

• Deploy and manage Kubernetes-based ML workloads
• Containerize pipelines using Docker
• Support scalable training and inference workflows

Establish Data Discipline

• Support feature engineering and dataset preparation
• Implement data versioning and governance practices (e.g., lakeFS)
• Apply metadata and data management standards

Create Repeatable Systems

• Develop runbooks, playbooks, and documentation
• Build systems that are operationally sustainable and transferable

What You Bring

Core Experience

• Experience deploying ML systems into production environments
• Strong programming skills in Python
• Hands-on experience with:
  
  • ML pipeline tools (Kubeflow, Airflow, Argo)
  • Experiment tracking tools (MLflow, ClearML)

Infrastructure & Systems

• Experience with Kubernetes and containerized systems (Docker)
• Familiarity with CI/CD pipelines
• Understanding of distributed systems and scalable architectures

ML Application Exposure

• Experience working with:
  
  • LLMs or transformer-based models
  • Computer vision systems (YOLO, Faster R-CNN)
• Focus on deployment and integration, not pure research

Mindset

• Systems thinker who prioritizes reliability over novelty
• Comfortable operating in complex, evolving environments
• Focused on delivering real-world outcomes

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:
  
  • U.S. citizens
  • eligible to obtain