MLOps Engineer

Das ist der Job

Implement CI/CD workflows for ML models and data pipelines in secure federal environments.

Darum lohnt es sich

Operationalize machine learning models built by data science teams and ensure production readiness. Overview As an MLOps Engineer, you will design, implement, and support the platforms, pipelines, and operational processes that enable scalable, secure, and reliable deployment of machine learning solutions for federal clients.

You will partner closely with data scientists, AI engineers, data engineers, and government stakeholders to operationalize models across development, testing, and production environments.

You will play a critical role in enabling secure AI and ML delivery within DoD and federal financial environments, ensuring models are repeatable, auditable, and compliant with federal standards. Design, build, and maintain end-to-end MLOps pipelines, supporting model training, testing, deployment, monitoring, and retraining.

Develop and manage model versioning, artifact management, and experiment tracking. Implement monitoring solutions for model performance, drift, data quality, and pipeline health. Automate infrastructure provisioning and deployment using infrastructure-as-code and DevOps best practices.

Support auditability, explainability, and governance of AI/ML systems. Collaborate with stakeholders to align MLOps architectures with mission needs and security requirements.

Responsibilities Design, build, and maintain end-to-end MLOps pipelines for model training, testing, deployment, monitoring, and retraining Implement CI/CD workflows for ML models and data pipelines in secure federal environments Operationalize machine learning models and ensure production readiness Develop and manage model versioning, artifact management, and experiment tracking Implement monitoring for model performance, drift, data quality, and pipeline health Automate infrastructure provisioning and deployment using infrastructure-as-code and DevOps best practices Support auditability, explainability, and governance of AI/ML systems Collaborate with stakeholders to align MLOps architectures with mission needs and security requirements Qualifications US Citizenship required Active and maintained SECRET Federal or DoD security clearance Bachelor’s degree 3–5 years of experience in MLOps, ML engineering, data engineering, DevOps, or related roles Strong experience with Python and ML tooling for packaging, deployment, and monitoring Hands-on experience building CI/CD pipelines for data and ML workloads Experience with containerization and orchestration (e.g., Docker, Kubernetes) Experience in secure cloud or hybrid environments supporting federal or DoD clients Familiarity with ML lifecycle management concepts including versioning, reproducibility, and monitoring Ability to communicate complex systems to both technical and non-technical audiences #J-18808-Ljbffr