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