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# AI/ML Platform Engineer

- **Company:** Johns Hopkins Applied Physics Laboratory
- **Location:** Laurel, MD, United States
- **Salary:** $155,000.0 - $195,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Airflow, Cloud Computing, Continuous Integration, Extract Transform Load (ETL), Linux, Monitoring of Systems, Machine Learning, Sage Accounting, Prometheus, Azure Machine Learning, Shell Script, Grafana, Apache Spark, Git, Containerization, Kubernetes, Infrastructure Automation Frameworks, Machine Learning Operations, Terraform, Data Pipelines, Docker, Jenkins
- **Published:** October 2, 2026
- **Apply:** https://find.jobs/jobs-near-me/apply/ats-redirect/?id=2997290480-2

## About the Role

* Machine learning platforms (Kubeflow, MLflow, Sage
 * Maker, Vertex AI, or similar)
 * MLOps and CI/CD for ML (Git
 * Lab CI, Git
 * Hub Actions, Jenkins, or similar)
 * Python for data/ML engineering
 * Containerization and orchestration (Docker, Kubernetes)
 * Cloud computing (AWS, Azure, or GCP)
 * Infrastructure as code (Terraform, Cloud
 * Formation, or similar)
 * Data pipelines and ETL (Spark, Airflow, or similar)
 * Monitoring and observability (Prometheus, Grafana, Cloud
 * Watch, etc.)
 * Linux systems and shell scripting
 * Security, compliance, and access control for data and ML systems

## Description

Johns Hopkins Applied Physics Laboratory (APL) seeks an AI/ML Platform Engineer to design, build, and operate secure, scalable machine learning platforms that power mission-critical research in national security, space, and health. You will create cloud-native and on-prem MLOps pipelines, automate infrastructure, and productionize models in partnership with data scientists and researchers. In APL's collaborative, mission-driven environment, you'll solve complex real-world problems while advancing your skills through research, advanced degrees, and professional development., * Design, build, and maintain scalable AI/ML platforms to support research and mission applications

 * Develop CI/CD and MLOps pipelines for model training, evaluation, and deployment
 * Implement observability, monitoring, and reliability practices for ML services
 * Collaborate with data scientists and engineers to productionize models and workflows
 * Optimize compute, storage, and data pipelines for performance and cost efficiency
 * Ensure security, compliance, and governance of AI/ML environments and data
 * Automate environment provisioning using infrastructure-as-code tools
 * Contribute to technical roadmaps, architecture decisions, and platform standards


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