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 SystemsAbout The RoleAt 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 Ofmachine learningcloud-native infrastructuredistributed systems…and ensure AI/ML systems are production-ready in environments where reliability and performance matter.What You’ll DoOwn the ML Lifecycle (End-to-End)Build and operate production-grade ML pipelinesOrchestrate workflows using Kubeflow, Airflow, or ArgoImplement model versioning, lineage, and reproducibility standardsOperationalize AI/ML SystemsDeploy models into secure and constrained environments Transition workflows from experimentation → containerized pipelines → production systems Enable both batch and real-time inference architecturesEngineer for ReliabilityDesign systems for reproducibility, auditability, and stabilityMonitor model performance and system health using Prometheus, Grafana, OpenTelemetryDetect and resolve issues such as model drift and system degradationBuild Cloud-Native ML InfrastructureDeploy and manage Kubernetes-based ML workloadsContainerize pipelines using DockerSupport scalable training and inference workflowsEstablish Data DisciplineSupport feature engineering and dataset preparationImplement data versioning and governance practices (e.g., lakeFS)Apply metadata and data management standardsCreate Repeatable SystemsDevelop runbooks, playbooks, and documentationBuild systems that are operationally sustainable and transferableWhat You BringCore Experience Experience deploying ML systems into production environmentsStrong programming skills in PythonHands-on experience with:ML pipeline tools (Kubeflow, Airflow, Argo)Experiment tracking tools (MLflow, ClearML)Infrastructure & SystemsExperience with Kubernetes and containerized systems (Docker)Familiarity with CI/CD pipelinesUnderstanding of distributed systems and scalable architecturesML Application Exposure Experience working with:LLMs or transformer-based modelsComputer vision systems (YOLO, Faster R-CNN)Focus on deployment and integration, not pure researchMindsetSystems thinker who prioritizes reliability over noveltyComfortable operating in complex, evolving environmentsFocused on delivering real-world outcomesClearance Requirements Active 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 environmentNote: Start timelines and work scope may vary depending on clearance status and program requirementsWhy This Role Matters (What You Get)This role is a career accelerator for engineers who want to:Move beyond experimentation and own production systemsWork across ML, infrastructure, and deployment pipelinesBuild in high-trust, secure environmentsDevelop high-demand MLOps expertise in constrained systemsDeliver systems that are used, not just builtWho We AreRackner 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 systemsDevSecOpsAI/MLCloud-native architectureOur approach is cloud-first, cost-effective, and outcome-driven, delivering systems that scale and perform in real-world environments.Benefits & Perks100% covered certifications & training aligned to your role401(k) with 100% match up to 6%Highly competitive PTOComprehensive Medical, Dental, Vision coverageLife Insurance + Short & Long-Term DisabilityHome office & equipment planIndustry-leading weekly pay scheduleApplyIf 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