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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 (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.

 

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

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