MLOps Engineer — AI/ML Systems & Deployment (TS/SCI Preferred)

<p><strong>MLOps Engineer — AI/ML Systems &amp; Deployment (TS/SCI Preferred)</strong><br>Dayton, OH (On-site Preferred) | Remote Eligible (U.S.-based, Clearance-Ready)<br>Clearance-Eligible Role | Mission-Critical AI/ML Systems</p> <p><strong>About the Role</strong></p> <p>At Rackner, we build systems where advanced technologies move beyond prototypes and into real-world operational use.</p> <p>We are seeking an MLOps Engineer to support the deployment and lifecycle management of AI/ML systems within a secure, mission-focused environment.</p> <p>This is not a research role.</p> <p>This is where models become reliable, deployable, and auditable systems.</p> <p>You will operate at the intersection of:</p> <ul> <li>machine learning</li> <li>cloud-native infrastructure</li> <li>distributed systems</li> </ul> <p>…and ensure AI/ML systems are production-ready in environments where reliability and performance matter.</p> <p><strong>What You’ll Do</strong></p> <p><strong>Own the ML Lifecycle (End-to-End)</strong></p> <ul> <li>Build and operate production-grade ML pipelines</li> <li>Orchestrate workflows using Kubeflow, Airflow, or Argo</li> <li>Implement model versioning, lineage, and reproducibility standards</li> </ul> <p><strong>Operationalize AI/ML Systems</strong></p> <ul> <li>Deploy models into secure and constrained environments<br>Transition workflows from experimentation → containerized pipelines → production systems<br>Enable both batch and real-time inference architectures</li> </ul> <p><strong>Engineer for Reliability</strong></p> <ul> <li>Design systems for reproducibility, auditability, and stability</li> <li>Monitor model performance and system health using Prometheus, Grafana, OpenTelemetry</li> <li>Detect and resolve issues such as model drift and system degradation</li> </ul> <p><strong>Build Cloud-Native ML Infrastructure</strong></p> <ul> <li>Deploy and manage Kubernetes-based ML workloads</li> <li>Containerize pipelines using Docker</li> <li>Support scalable training and inference workflows</li> </ul> <p><strong>Establish Data Discipline</strong></p> <ul> <li>Support feature engineering and dataset preparation</li> <li>Implement data versioning and governance practices (e.g., lakeFS)</li> <li>Apply metadata and data management standards</li> </ul> <p><strong>Create Repeatable Systems</strong></p> <ul> <li>Develop runbooks, playbooks, and documentation</li> <li>Build systems that are operationally sustainable and transferable</li> </ul> <p><strong>What You Bring</strong></p> <p><strong>Core Experience</strong></p> <ul> <li>Experience deploying ML systems into production environments</li> <li>Strong programming skills in Python</li> <li>Hands-on experience with:<br> <ul> <li>ML pipeline tools (Kubeflow, Airflow, Argo)</li> <li>Experiment tracking tools (MLflow, ClearML)</li> </ul> </li> </ul> <p><strong>Infrastructure &amp; Systems</strong></p> <ul> <li>Experience with Kubernetes and containerized systems (Docker)</li> <li>Familiarity with CI/CD pipelines</li> <li>Understanding of distributed systems and scalable architectures</li> </ul> <p><strong>ML Application Exposure</strong></p> <ul> <li>Experience working with:<br> <ul> <li>LLMs or transformer-based models</li> <li>Computer vision systems (YOLO, Faster R-CNN)</li> </ul> </li> <li>Focus on deployment and integration, not pure research</li> </ul> <p><strong>Mindset</strong></p> <ul> <li>Systems thinker who prioritizes reliability over novelty</li> <li>Comfortable operating in complex, evolving environments</li> <li>Focused on delivering real-world outcomes</li> </ul> <p><strong>Clearance Requirements</strong></p> <ul> <li>Active TS/SCI clearance strongly preferred</li> <li>Candidates with an active Secret clearance may be considered and supported for upgrade</li> <li>Candidates without an active clearance must be:<br> <ul> <li>U.S. citizens</li> <li>eligible to obtain and maintain a clearance</li> <li>able to work in a CAC-enabled or secure environment</li> </ul> </li> </ul> <p><strong>Note:</strong>&nbsp;Start timelines and work scope may vary depending on clearance status and program requirements</p> <p><strong>Why This Role Matters (What You Get)</strong></p> <p>This role is a career accelerator for engineers who want to:</p> <ul> <li>Move beyond experimentation and own production systems</li> <li>Work across ML, infrastructure, and deployment pipelines</li> <li>Build in high-trust, secure environments</li> <li>Develop high-demand MLOps expertise in constrained systems</li> <li>Deliver systems that are used, not just built</li> </ul> <p><strong>Who We Are</strong></p> <p>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:</p> <ul> <li>Distributed systems</li> <li>DevSecOps</li> <li>AI/ML</li> <li>Cloud-native architecture</li> </ul> <p>Our approach is cloud-first, cost-effective, and outcome-driven, delivering systems that scale and perform in real-world environments.</p> <p><strong>Benefits &amp; Perks</strong></p> <ul> <li>100% covered certifications &amp; training aligned to your role</li> <li>401(k) with 100% match up to 6%</li> <li>Highly competitive PTO</li> <li>Comprehensive Medical, Dental, Vision coverage</li> <li>Life Insurance + Short &amp; Long-Term Disability</li> <li>Home office &amp; equipment plan</li> <li>Industry-leading weekly pay schedule</li> </ul> <p><strong>Apply</strong></p> <p>If you’re an engineer who wants to move from building models → owning production systems, we’d like to connect.</p> <p>&nbsp;</p> <p>#MLOps #MachineLearning #Kubernetes #AIEngineering #CloudNative #DevSecOps #ArtificialIntelligence #DataEngineering #DefenseTech #NationalSecurity #AIInfrastructure #Hiring #TechCareers</p>