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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # MLOps Engineer - AI/ML Systems Deployment (TS/SCI Preferred) - **Company:** Rackner, Inc. - **Location:** Cincinnati, OH, United States - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Airflow, Computer Vision, Cloud Computing, Cloud Engineering, Program Optimization, Computer Programming, Continuous Integration, Monitoring of Systems, Python (Programming Language), Machine Learning, Prometheus, Software Engineering, Delivery Pipeline, Large Language Models, Grafana, SC Clearance, Build Management, Containerization, Kubernetes, Build Tools, Machine Learning Operations, GPT, Docker - **Published:** June 30, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=0b62aba9300f080d ## About the Role Do you have experience in Technology infrastructure engineering?, 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, * 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, * 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 ## Description 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 ## Related Videos - [DevOps for AI: running LLMs in production with Kubernetes and KubeFlow](https://www.wearedevelopers.com/videos/1222-devops-for-ai-running-llms-in-production-with-kubernetes-and-kubeflow) - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [ Evaluating AI models for code comprehension](https://www.wearedevelopers.com/videos/1462-evaluating-ai-models-for-code-comprehension) - [AI Model Management Life Circles: ML Ops For Generative AI Models From Research to Deployment](https://www.wearedevelopers.com/videos/1152-ai-model-management-life-circles-ml-ops-for-generative-ai-models-from-research-to-deployment) - [Docker build without Docker](https://www.wearedevelopers.com/videos/100114-docker-build-without-docker) ## Related Articles - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [From Prototype to Production: Build AI Agents with This Free 4-Course Learning Path](https://www.wearedevelopers.com/magazine/655-from-prototype-to-production-build-ai-agents-with-this-free-4-course-learning-path)