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

Job Description MLOps Engineer — AI/ML Systems Deployment Location: Dayton, OH preferred; Cleveland, OH may be considered Engineering & Technology 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. U.S. citizenship required. Job Description Build and Deploy Real-World AI Systems. Rackner is hiring an MLOps Engineer to move AI/ML systems from prototype → deployment → operational use in a secure, mission-focused environment. This is not a research role—this is where models become reliable, repeatable, auditable systems that run in real-world conditions. Jobs 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; and deliver technology that is used, trusted, and operational. Key Responsibilities Operationalize AI/ML systems by deploying models into secure environments, moving workflows into containerized pipelines, and supporting batch/real-time inference architectures. Own the ML lifecycle by building production-grade pipelines, managing model versioning and lineage, and using tools like MLflow, Kubeflow, Airflow, Argo, or ClearML. Build cloud-native ML infrastructure on Kubernetes, containerize models with Docker, and support CI/CD for AI/ML systems. Engineer for reliability by monitoring system performance with tools like Prometheus, Grafana, or OpenTelemetry, and resolving issues related to latency, drift, or resource usage. Support secure/constrained environments with limited compute, restricted data, or degraded connectivity. Create repeatable systems through runbooks, documentation, and operational playbooks. Qualifications Core Experience: U.S. citizenship, background in deploying ML systems or production software, strong Python skills, hands-on Docker/container experience, familiarity with Kubernetes or cloud-native environments, understanding of CI/CD, clear communication, and ability to work in secure/CAC-enabled environments. Preferred Qualifications: Active TS/SCI clearance, active Secret with upgrade eligibility, experience with ML lifecycle tools (MLflow, Kubeflow, etc.), model serving/inference APIs, LLMs/transformers, Kubernetes-based ML workloads, observability tools, DoD/defense background, and exposure to edge/offline environments. Clearance Requirements: Active TS/SCI strongly preferred; active Secret may be considered; candidates without clearance must be U.S. citizens eligible to obtain/maintain clearance and work in secure environments. Note: Start timelines may vary based on clearance status. Company Overview Rackner is a software consultancy building cloud-native solutions for startups, enterprises, and the public sector, focusing on distributed systems, DevSecOps, AI/ML, and cloud-native architecture. Work & Labor Issues Benefits 100% covered certifications & training 401(k) with 100% match up to 6% Highly competitive PTO Comprehensive Medical, Dental, Vision coverage Life Insurance + Short & Long-Term Disability Home office & equipment plan Industry-leading weekly pay schedule Application: 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. Engineering & Technology