ML Ops Engineer
Core
Develop and maintain machine learning pipelines, model registries, deployment frameworks, and automated workflows to operationalize AI/ML capabilities for a large federal agency's enterprise data ecosystem.
Role type
Senior MLOps Engineer
Builds
Production-grade MLOps pipelines, model serving infrastructure, and automated CI/CD workflows for enterprise AI initiatives.
Domain
Federal government / Cloud AI & ML
Deliverable
production ML models
Required skills
MLOps, Python, Azure Databricks, MLflow, Unity Catalog, CI/CD, Terraform, model monitoring, cost optimization
Preferred skills
LLMOps, Responsible AI governance, infrastructure-as-code, cloud cost optimization
Technologies
Azure Databricks, Databricks ML, MLflow, Unity Catalog, Azure Machine Learning, GitHub Actions, Azure DevOps, Terraform
Responsibilities
Design and maintain production-grade MLOps pipelines for model training, evaluation, validation, registration, deployment, and monitoring; Implement automated CI/CD pipelines to promote models across environments; Deploy and maintain scalable model-serving infrastructure for batch, streaming, and real-time workloads; Establish automated testing and evaluation gates for model performance, data quality, and safety; Implement model and feature monitoring, drift detection, and operational dashboards; Develop automated retraining, champion/challenger promotion, and model-retirement workflows; Partner with data scientists and DevSecOps to ensure models are repeatable and operationally supportable; Monitor compute utilization and implement cost-optimization strategies.
Seniority
Senior, hands-on IC
