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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
Core Qualifications
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
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 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