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

Job DescriptionBuild 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.Location: Dayton, OH preferred; Cleveland, OH may be consideredWork Arrangement: On-site preferred; remote may be considered for highly aligned, clearance‑ready candidates able to support secure / CAC-enabled environments and travel as neededClearance: Active TS/SCI strongly preferred; active Secret may be considered for upgrade. U.S. citizenship required.Development ToolsThis 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 ResponsibilitiesOperationalize 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.QualificationsCore 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 OverviewRackner 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.Benefits100% covered certifications & training401(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 scheduleApplicationIf you are an engineer who wants to move from building models or platforms to owning deployed AI/ML systems, we would like to connect.#J-18808-Ljbffr

Government Careers

Show more