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 and maintain a clearance
  
  
  • able to work in a CAC-enabled or secure environment
  
  
  
  

Note: Start timelines and work scope may vary depending on clearance status and program requirements

Why This Role Matters (What You Get)

This role is a career accelerator for engineers who want to:

• Move beyond experimentation and own production systems


• Work across ML, infrastructure, and deployment pipelines


• Build in high-trust, secure environments


• Develop high-demand MLOps expertise in constrained systems


• Deliver systems that are used, not just built

Who We Are

Rackner is a software consultancy that builds cloud-native solutions for startups, enterprises, and the public sector. We are an energetic, growing team focused on solving complex problems through:

• Distributed systems


• DevSecOps


• AI/ML


• Cloud-native architecture

Our approach is cloud-first, cost-effective, and outcome-driven, delivering systems that scale and perform in real-world environments.

Benefits & Perks

• 100% covered certifications & training aligned to your role


• 401(k) with 100% match up to 6%


• Highly competitive PTO


• Comprehensive Medical, Dental, Vision coverage


• Life Insurance + Short & Long-Term Disability


• Home office & equipment plan


• Industry-leading weekly pay schedule

Apply

If you’re an engineer who wants to move from building models → owning production systems, we’d like to connect.

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