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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:** United States (Remote available) - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Airflow, Computer Vision, Audit Trail, Cloud Engineering, Computer Programming, Distributed Systems, Python (Programming Language), Machine Learning, Metadata, Prometheus, Management of Software Versions, Feature Engineering, Delivery Pipeline, Large Language Models, Grafana, SC Clearance, Containerization, Kubernetes, Build Tools, Data Management, Machine Learning Operations, GPT, Software Version Control, Docker - **Published:** May 19, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=891130b85cfe5df5 ## About the Role * Experience deploying ML systems into production environments * Strong programming skills in Python * Hands-on experience with: + ML pipeline tools (Kubeflow, Airflow, Argo) + Experiment tracking tools (MLflow, ClearML) Infrastructure & Systems * Experience with Kubernetes and containerized systems (Docker) * Familiarity with CI/CD pipelines * Understanding of distributed systems and scalable architectures ML Application Exposure * Experience working with: + LLMs or transformer-based models + Computer vision systems (YOLO, Faster R-CNN) * Focus on deployment and integration, not pure research Mindset * Systems thinker who prioritizes reliability over novelty * Comfortable operating in complex, evolving environments * Focused on delivering real-world outcomes 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 At Rackner, we build systems where advanced technologies move beyond prototypes and into real-world operational use., We are seeking an MLOps Engineer to support the deployment and lifecycle management of AI/ML systems within a secure, mission-focused environment. This is not a research role. This is where models become reliable, deployable, and auditable systems. You will operate at the intersection of: * machine learning * cloud-native infrastructure * distributed systems …and ensure AI/ML systems are production-ready in environments where reliability and performance matter. What You'll Do Own the ML Lifecycle (End-to-End) * Build and operate production-grade ML pipelines * Orchestrate workflows using Kubeflow, Airflow, or Argo * Implement model versioning, lineage, and reproducibility standards Operationalize AI/ML Systems * Deploy models into secure and constrained environments Transition workflows from experimentation containerized pipelines production systems Enable both batch and real-time inference architectures Engineer for Reliability * Design systems for reproducibility, auditability, and stability * Monitor model performance and system health using Prometheus, Grafana, OpenTelemetry * Detect and resolve issues such as model drift and system degradation Build Cloud-Native ML Infrastructure * Deploy and manage Kubernetes-based ML workloads * Containerize pipelines using Docker * Support scalable training and inference workflows Establish Data Discipline * Support feature engineering and dataset preparation * Implement data versioning and governance practices (e.g., lakeFS) * Apply metadata and data management standards Create Repeatable Systems * Develop runbooks, playbooks, and documentation * Build systems that are operationally sustainable and transferable, This role is a career accelerator for engineers who want to: * Move beyond experimentation and own production systems * Work across ML, infrastructure, and deployment pipelines * Build in high-trust, secure environments * Develop high-demand MLOps expertise in constrained systems * Deliver systems that are used, not just built ## Related Videos - [Effective Machine Learning - Managing Complexity with MLOps](https://www.wearedevelopers.com/videos/185-effective-machine-learning-managing-complexity-with-mlops) - [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 – 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) - [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) - [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)