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 Apply To This Job