MLOps Engineer — AI​/ML Systems & Deployment; TS​/SCI

• system health & latency
• computer vision systems (YOLO, Faster R-CNN)

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

Mission Environment | AI/ML Infrastructure | National Security Impact

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.

• Machine learning
• Distributed systems
• Cloud-native infrastructure

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

• Build and operate production-grade ML pipelines
• Orchestrate workflows using Kubeflow, Airflow, or Argo
• Implement model versioning, lineage, and reproducibility standards

• 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

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

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

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

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

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

• Experience with Kubernetes and containerized workloads
• Familiarity with CI/CD for ML systems
• Understanding of distributed systems and scalable architectures

• Experience working with:
  • LLMs or transformer‑based models
  • computer vision systems (YOLO, Faster R‑CNN)
• Focus on deployment and integration, not pure research

• Systems thinker who values reliability over novelty
• Comfortable operating in ambiguous, high‑stakes environments
• Able to translate experimental work into operational capability

• 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

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.

• Distributed systems
• Dev Sec Ops
• AI/ML
• Cloud‑native architecture

• 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

If you’re an engineer who wants to move from building models → owning systems, we want to talk.