MLOps Engineer - AI/ML Systems Deployment (TS/SCI

Clearance: Active TS/SCI strongly preferred; active Secret may be considered for upgrade Requirement: U.S. citizenship required Build AI/ML Systems That Move From Prototype to Mission Use Rackner is seeking an MLOps Engineer to help operationalize AI/ML systems in a secure, mission-focused environment. This is not a pure research role. This is where AI/ML capabilities move from prototype * deployment * operational use. You will help build systems that are reliable, repeatable, auditable, and ready to run in real-world environments where performance, trust, and mission outcomes matter. 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 Help deliver technology that is used, trusted, and operational Grow in a technical lane that sits at the intersection of AI, cloud-native engineering, and national security What You'll Do Operationalize AI/ML Systems Deploy AI/ML models and ML-enabled applications into secure, real-world environments Move workflows from experimentation into containerized, repeatable deployment pipelines Support batch and real-time inference architectures Bridge model development, software engineering, and platform operations Own the ML Lifecycle Build and operate production-grade ML pipelines Support model versioning, lineage, reproducibility, and lifecycle governance Work with tools such as MLflow, Kubeflow, Airflow, Argo, ClearML, or similar platforms Build Cloud-Native ML Infrastructure Deploy and support Kubernetes-based ML workloads Containerize models, pipelines, and services using Docker or similar tools Support CI/CD, automation, and repeatable deployment patterns for AI/ML systems Engineer for Reliability Monitor model and system performance after deployment Support observability using tools such as Prometheus, Grafana, OpenTelemetry, or similar Detect and resolve issues related to latency, reliability, drift, degradation, or resource usage Support Secure and Constrained Environments Help deploy AI/ML systems in secure, CAC-enabled, or constrained environments Support limited compute, restricted data, degraded connectivity, and other operational constraints Optimize systems for reliability and usability beyond ideal lab conditions Create Repeatable Systems Develop runbooks, deployment documentation, and operational playbooks Build systems that can be understood, maintained, and operated by others What You Bring, 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 This role gives you the opportunity to work in a rare technical lane: AI/ML deployment for secure, mission-focused systems. You will gain experience that is difficult to find in traditional commercial MLOps roles, including: AI/ML operationalization in high-trust environments Deployment into secure or constrained systems Cross-functional work across ML, software, platform, and mission teams Cloud-native MLOps using modern infrastructure and automation practices Systems where reliability, reproducibility, and operational value matter If you want your work to move beyond demos and into real-world use, this role is built for that. 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 Secure systems delivery Our approach is cloud-first, cost-effective, and outcome-driven. We build systems that scale, perform, and support real-world operational needs. Benefits & Perks 100% covered certifications and training aligned to your role 401(k) with 100% match up to 6% Highly competitive PTO Comprehensive Medical, Dental, and Vision coverage Life Insurance Short-Term and Long-Term Disability Home office and equipment plan Industry-leading weekly pay schedule Apply 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. Search Keywords MLOps, Machine Learning Operations, ML Platform Engineer, AI Infrastructure Engineer, AI/ML Engineer, Machine Learning Engineer, Kubernetes, Docker, Python, MLflow, Kubeflow, Airflow, Argo, ClearML, model deployment, model serving, inference, AI/ML systems, TS/SCI, Secret clearance, DoD, defense, mission systems, DevSecOps, cloud-native, constrained environments, edge AI, secure systems MLOps Engineer - AI/ML Systems Deployment (TS/SCI Preferred) Location: Dayton, OH preferred 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 Requirement: U.S. citizenship required Build AI/ML Systems That Move From Prototype to Mission Use Rackner is seeking an MLOps Engineer to help operationalize AI/ML systems in a secure, mission-focused environment. This is not a pure research role. This is where AI/ML capabilities move from prototype * deployment * operational use. You will help build systems that are reliable, repeatable, auditable, and ready to run in real-world environments where performance, trust, and mission outcomes matter. 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 Help deliver technology that is used, trusted, and operational Grow in a technical lane that sits at the intersection of AI, cloud-native engineering, and national security What You'll Do Operationalize AI/ML Systems Deploy AI/ML models and ML-enabled applications into secure, real-world environments Move workflows from experimentation into containerized, repeatable deployment pipelines Support batch and real-time inference architectures Bridge model development, software engineering, and platform operations Own the ML Lifecycle Build and operate production-grade ML pipelines Support model versioning, lineage, reproducibility, and lifecycle governance Work with tools such as MLflow, Kubeflow, Airflow, Argo, ClearML, or similar platforms Build Cloud-Native ML Infrastructure Deploy and support Kubernetes-based ML workloads Containerize models, pipelines, and services using Docker or similar tools Support CI/CD, automation, and repeatable deployment patterns for AI/ML systems Engineer for Reliability Monitor model and system performance after deployment Support observability using tools such as Prometheus, Grafana, OpenTelemetry, or similar Detect and resolve issues related to latency, reliability, drift, degradation, or resource usage Support Secure and Constrained Environments Help deploy AI/ML systems in secure, CAC-enabled, or constrained environments Support limited compute, restricted data, degraded connectivity, and other operational constraints Optimize systems for reliability and usability beyond ideal lab conditions Create Repeatable Systems Develop runbooks, deployment documentation, and operational playbooks Build systems that can be understood, maintained, and operated by others What You Bring, 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 This role gives you the opportunity to work in a rare technical lane: AI/ML deployment for secure, mission-focused systems. You will gain experience that is difficult to find in traditional commercial MLOps roles, including: AI/ML operationalization in high-trust environments Deployment into secure or constrained systems Cross-functional work across ML, software, platform, and mission teams Cloud-native MLOps using modern infrastructure and automation practices Systems where reliability, reproducibility, and operational value matter If you want your work to move beyond demos and into real-world use, this role is built for that. 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 Secure systems delivery Our approach is cloud-first, cost-effective, and outcome-driven. We build systems that scale, perform, and support real-world operational needs. Benefits & Perks 100% covered certifications and training aligned to your role 401(k) with 100% match up to 6% Highly competitive PTO Comprehensive Medical, Dental, and Vision coverage Life Insurance Short-Term and Long-Term Disability Home office and equipment plan Industry-leading weekly pay schedule Apply 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. Search Keywords MLOps, Machine Learning Operations, ML Platform Engineer, AI Infrastructure Engineer, AI/ML Engineer, Machine Learning Engineer, Kubernetes, Docker, Python, MLflow, Kubeflow, Airflow, Argo, ClearML, model deployment, model serving, inference, AI/ML systems, TS/SCI, Secret clearance, DoD, defense, mission systems, DevSecOps, cloud-native, constrained environments, edge AI, secure systems

U.S. citizenship Background in deploying ML systems, AI-enabled applications, or production software Strong programming skills in Python Hands-on work with Docker, containers, or containerized deployment Familiarity with Kubernetes or cloud-native environments Understanding of CI/CD, automation, or pipeline-based delivery Clear communication of technical decisions, tradeoffs, and ownership Ability to operate in a CAC-enabled or secure environment Preferred Qualifications Active TS/SCI clearance Active Secret clearance with eligibility for upgrade Familiarity with ML lifecycle tools such as MLflow, Kubeflow, Airflow, Argo, ClearML, or similar Background in model serving, inference APIs, or deploying ML systems in production Exposure to LLMs, transformer-based models, computer vision, NLP, or applied AI solutions Hands-on work with Kubernetes-based ML workloads Knowledge of observability and monitoring tools such as Prometheus, Grafana, or OpenTelemetry Experience in DoD, defense, intelligence, regulated, or mission-critical settings Experience with edge, offline, air-gapped, low-bandwidth, D-DIL, or limited-compute environments