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

Dayton, OH (On-site Preferred) | Remote Eligible (CAC-Ready Candidates)

Mission Environment | AI/ML Infrastructure | National Security Impact

About The Role

At Rackner, we are building the operational backbone that turns AI/ML capability into real-world mission outcomes. We are seeking an MLOps Engineer to own the lifecycle of AI/ML systems—from experimentation to deployment—within a mission-critical, classified environment supporting Air Force and NASIC-aligned programs.

This is not a research role; This is where models become reliable, deployable, auditable systems.

You Will Operate At The Intersection Of

• Machine learning
• Distributed systems
• Cloud-native infrastructure
  
  

…and ensure that AI/ML systems work in the environments where failure is not an option.

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 mission environments (including constrained or classified systems)
• Transition workflows from Jupyter experimentation → containerized pipelines → production systems
• Enable both batch and real-time inference architectures
  
  

Engineer for Reliability, Not Just Performance

• Design systems for reproducibility, auditability, and stability
• Implement monitoring for:
• model performance & drift
• system health & latency
• Use tools like Prometheus, Grafana, and OpenTelemetry
  

Build Cloud-Native ML Infrastructure

• Deploy and manage Kubernetes-based ML workloads
• Containerize pipelines using Docker / OCI standards
• Scale compute for training and inference workloads
  
  

Establish Data Discipline

• Enable data versioning and governance (lakeFS or similar)
• Support feature engineering and dataset preparation pipelines
• Apply metadata standards (e.g., STAC) where applicable
  
  

Create Repeatable Systems

• Develop runbooks, playbooks, and deployment standards
• Build systems that can be operated by others; not just understood by you
  
  

What You Bring

Core Experience

• Experience deploying ML systems into production environments
• Strong background in Python and ML frameworks (PyTorch, TensorFlow, etc.)
• Hands-on experience with:
• ML pipeline orchestration tools (Kubeflow, Airflow, Argo)
• Experiment tracking (MLflow, ClearML)
  

Infrastructure & Systems

• Experience with Kubernetes and containerized workloads
• Familiarity with CI/CD for ML systems
• 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 values reliability over novelty
• Comfortable operating in ambiguous, high-stakes environments
• Able to translate experimental work into operational capability
  
  

Why This Role Matters (What You Get)

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

• Move beyond experimentation
• Own systems that actually get deployed and used
• Operate at the systems level
• Work across ML, infrastructure, and mission integration
• Build in high-trust environments
• Where correctness, auditability, and reliability matter
• Develop rare, high-demand expertise
• MLOps in constrained / classified environments is a differentiated skillset
  

Shape how AI is operationalized—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 consultancy with a passion for solving big problems across industries.

We Enable Digital Transformation Through

• Distributed systems
• DevSecOps
• AI/ML
• Cloud-native architecture
  
  

Our approach is cloud-first, cost-effective, and outcome-driven—focused on delivering real capability, not just code.

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 systems, we want to talk.

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