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

Job Description

Build and Deploy Real-World AI Systems. Rackner is hiring an MLOps Engineer to move AI/ML systems from prototype → deployment → operational use in a secure, mission-focused environment. This is not a research role—this is where models become reliable, repeatable, auditable systems that run in real-world conditions.

Location: Dayton, OH preferred;
Cleveland, OH may be considered

Work Arrangement: On-site preferred; remote may be considered for highly aligned, clearance‑ready candidates able to support secure / CAC-enabled environments and travel as needed

Clearance: Active TS/SCI strongly preferred; active Secret may be considered for upgrade. U.S. citizenship required.

This role is ideal for engineers who want to work across AI/ML, Kubernetes, infrastructure, and mission systems; own deployed systems, not just experiments; build high-demand MLOps expertise in secure and constrained environments; and deliver technology that is used, trusted, and operational.

• Operationalize AI/ML systems by deploying models into secure environments, moving workflows into containerized pipelines, and supporting batch/real-time inference architectures.
• Own the ML lifecycle by building production‑grade pipelines, managing model versioning and lineage, and using tools like MLflow, Kubeflow, Airflow, Argo, or ClearML.
• Build cloud‑native ML infrastructure on Kubernetes, containerize models with Docker, and support CI/CD for AI/ML systems.
• Engineer for reliability by monitoring system performance with tools like Prometheus, Grafana, or Open Telemetry, and resolving issues related to latency, drift, or resource usage.
• Support secure/constrained environments with limited compute, restricted data, or degraded connectivity.
• Create repeatable systems through runbooks, documentation, and operational playbooks.

• Core
  
  Experience:
  
  U.S. citizenship, background in deploying ML systems or production software, strong Python skills, hands‑on Docker/container experience, familiarity with Kubernetes or cloud‑native environments, understanding of CI/CD, clear communication, and ability to work in secure/CAC-enabled environments.
• Preferred Qualifications:
  
  Active TS/SCI clearance, active Secret with upgrade eligibility, experience with ML lifecycle tools (MLflow, Kubeflow, etc.), model serving/inference APIs, LLMs/transformers, Kubernetes-based ML workloads, observability tools, DoD/defense background, and exposure to edge/offline environments.
• Clearance Requirements:
  Active TS/SCI strongly preferred; active Secret may be considered; candidates without clearance must be U.S. citizens eligible to obtain/maintain clearance and work in secure environments.

Note:

Start timelines may vary based on clearance status.

Rackner is a software consultancy building cloud-native solutions for startups, enterprises, and the public sector, focusing on distributed systems, Dev Sec Ops , AI/ML, and cloud-native architecture.

• 100% covered certifications & training
• 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 are an engineer who wants to move from building models or platforms to owning deployed AI/ML systems, we would like to connect.