Mlops Engineer — AI ML Systems Deployment

MLOps Engineer — AI/ML Systems Deployment Location: Dayton, OH preferred 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 Requirement: U.S. citizenship required 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. 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 • Deliver technology that is used, trusted, and operational You will help operationalize AI/ML capabilities where reliability, performance, and trust matter most. What You’ll Do Operationalize AI/ML Systems • Deploy AI/ML models and ML-enabled applications into secure, real-world environments • Move workflows from experimentation into containerized, repeatable deployment pipelines • Support batch and real-time inference architectures • Bridge model development, software engineering, and platform operations Own the ML Lifecycle • Build and operate production-grade ML pipelines • Support model versioning, lineage, reproducibility, and lifecycle governance • Work with tools such as MLflow, Kubeflow, Airflow, Argo, ClearML, or similar platforms Build Cloud-Native ML Infrastructure • Deploy and support Kubernetes-based ML workloads • Containerize models, pipelines, and services using Docker or similar tools • Support CI/CD, automation, and repeatable deployment patterns for AI/ML systems Engineer for Reliability • Monitor model and system performance after deployment • Support observability using tools such as Prometheus, Grafana, OpenTelemetry, or similar • Detect and resolve issues related to latency, reliability, drift, degradation, or resource usage Support Secure and Constrained Environments • Help deploy AI/ML systems in secure, CAC-enabled, or constrained environments • Support limited compute, restricted data, degraded connectivity, and other operational constraints • Optimize systems for reliability and usability beyond ideal lab conditions Create Repeatable Systems • Develop runbooks, deployment documentation, and operational playbooks • Build systems that can be understood, maintained, and operated by others What You Bring Core Experience • U.S. citizenship • Background in deploying ML systems, AI-enabled applications, or production software • Strong programming skills in Python • Hands-on work with Docker, containers, or containerized deployment • Familiarity with Kubernetes or cloud-native environments • Understanding of CI/CD, automation, or pipeline-based delivery • Clear communication of technical decisions, tradeoffs, and ownership • Ability to operate in a CAC-enabled or secure environment Preferred Qualifications • Active TS/SCI clearance • Active Secret clearance with eligibility for upgrade • Familiarity with ML lifecycle tools such as MLflow, Kubeflow, Airflow, Argo, ClearML, or similar • Background in model serving, inference APIs, or deploying ML systems in production • Exposure to LLMs, transformer-based models, computer vision, NLP, or applied AI solutions • Hands-on work with Kubernetes-based ML workloads • Knowledge of observability and monitoring tools such as Prometheus, Grafana, or OpenTelemetry • Experience in DoD, defense, intelligence, regulated, or mission-critical settings • Work in edge, offline, air-gapped, low-bandwidth, D-DIL, or limited-compute environments Clearance Requirements • Active TS/SCI clearance strongly preferred • Candidates with an active Secret clearance may be considered and supported for upgrade • Candidates without an active clearance must be: • U.S. citizens • eligible to obtain and maintain a clearance • able to work in a CAC-enabled or secure environment Note: Start timelines and work scope may vary depending on clearance status and program requirements Who We Are 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: • Distributed systems • DevSecOps • AI/ML • Cloud-native architecture Our approach is cloud-first, cost-effective, and outcome-driven, delivering systems that scale and perform in real-world environments. Benefits & Perks • 100% covered certifications & training aligned to your role • 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 Apply 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.