MLOps Engineer - AI/ML Systems & Deployment (TS/SCI Preferred) with Security Clearance

MLOps Engineer — AI/ML Systems & Deployment (TS/SCI Preferred) Location: Dayton, OH (On-site Preferred) | Remote Eligible (U.S.-based, Clearance-Ready)Clearance: TS/SCI Preferred | Secret Eligible Overview Rackner is seeking an MLOps Engineer to support the deployment and lifecycle management of AI/ML systems within a secure, mission-focused environment. This role is responsible for operationalizing machine learning capabilities—moving models from experimentation into reliable, deployable, and auditable systems. You will work across: machine learningcloud-native infrastructuredistributed systems …to ensure AI/ML systems are production-ready in environments where reliability, performance, and security are critical. ResponsibilitiesBuild and maintain production ML pipelines using tools such as Kubeflow, Airflow, or ArgoDeploy ML models into secure and constrained environments (including on-prem, air-gapped, or hybrid systems)Implement model versioning, reproducibility, and lifecycle management (MLflow, ClearML)Develop and operate containerized ML workloads using Docker and KubernetesDesign and support model serving architectures (batch and real-time inference)Monitor system and model performance using Prometheus, Grafana, OpenTelemetrySupport data preparation, feature engineering, and dataset versioning (lakeFS or similar)Create technical documentation, runbooks, and operational standardsCollaborate with cross-functional teams to ensure successful integration into operational systems Required QualificationsU.S. Citizenship (required for clearance eligibility)Experience deploying ML systems into production environmentsStrong programming skills in PythonExperience with Kubernetes and containerized systems (Docker) Hands-on experience with:ML pipeline tools (Kubeflow, Airflow, Argo)Model tracking/versioning tools (MLflow, ClearML) Understanding of distributed systems and scalable architecturesExperience with cloud platforms (AWS, Azure, or GCP) Preferred QualificationsActive TS/SCI clearanceExperience with LLMs, transformer-based models, or computer vision systemsFamiliarity with model serving frameworks and inference optimizationExperience working in regulated, defense, or mission-critical environmentsExposure to data versioning tools (lakeFS) and metadata standardsExperience supporting systems in air-gapped or secure environments Clearance RequirementsActive TS/SCI clearance strongly preferredCandidates with an active Secret clearance may be considered and supported for upgradeCandidates without an active clearance must be:U.S. citizenseligible to obtain and maintain a clearanceable to work in a CAC-enabled or secure environment Note: Start timelines and work scope may vary depending on clearance status and program requirements. What Sets This Role ApartWork on AI/ML systems that are deployed and used in real-world environmentsBuild systems that prioritize reliability, reproducibility, and operational impactGain experience operating within secure, high-trust environmentsCollaborate on modern MLOps, DevSecOps, and cloud-native architectures About RacknerRackner is a software consultancy that builds cloud-native solutions for startups, enterprises, and the public sector. We specialize in: cloud-native developmentDevSecOpsAI/ML systemsdistributed architecture Our approach is cloud-first, cost-effective, and outcome-driven, delivering scalable and resilient systems. Benefits401(k) with 100% match up to 6%Comprehensive Medical, Dental, Vision coverageLife Insurance + Short & Long-Term DisabilityGenerous PTOWeekly pay scheduleHome office & equipment supportCertification and training reimbursement ApplyIf you're an engineer who wants to move from building models ? owning production systems, we'd like to connect: MLOps, Machine Learning Operations, Kubernetes, Docker, Kubeflow, MLflow, Airflow, Argo Workflows, Python, AI/ML, Model Deployment, Model Serving, DevSecOps, Cloud, TS/SCI, Clearance