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

Dayton, OH

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.

 

#MLOps #MachineLearning #Kubernetes #AIEngineering #CloudNative #DevSecOps #ArtificialIntelligence #DataEngineering #DefenseTech #NationalSecurity #AIInfrastructure #Hiring #TechCareers

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