ML Ops Engineer

Job summary

The MLOps Engineer operationalizes machine learning across a cloud-based federal data and analytics platform. The role develops production pipelines for model training, deployment, monitoring, retraining, and retirement; builds CI/CD and model-serving frameworks; and implements Trustworthy AI controls aligned with the NIST AI Risk Management Framework. Key technologies include Azure Databricks, MLflow, Azure Machine Learning, Python, GitHub Actions, Azure DevOps, and Terraform.

Qualifications

• U.S. citizenship required.
• Must reside in the United States, be authorized to work there, and perform all work in the United States.
• Must have lived in the United States for three full years within the last five years.
• At least six years of experience designing and supporting production machine learning solutions in cloud environments.
• Bachelor's degree in a relevant field, or a high school diploma plus four additional years of relevant experience.
• Experience with MLOps, automated deployment pipelines, and model lifecycle management.
• Proficiency in Python and modern software development practices.
• Experience with source control systems and CI/CD frameworks.
• Hands-on experience with Azure Machine Learning, Databricks, MLflow, or comparable platforms.
• Experience with model monitoring, automated retraining, and performance tracking.
• Knowledge of cloud-native architecture, containerization, and scalable model serving.
• Experience with Responsible AI, AI governance, or Trustworthy AI.
• Familiarity with infrastructure as code and automated environment provisioning.
• Understanding of cloud cost optimization for AI and machine learning workloads.

Responsibilities

• Design and maintain production pipelines for model training, evaluation, deployment, monitoring, retraining, rollback, and retirement.
• Develop reusable MLOps and LLMOps patterns using Azure Databricks, MLflow, Unity Catalog, Azure Machine Learning, and Python.
• Implement experiment tracking, model versioning, registries, lineage, model cards, and approval workflows.
• Build CI/CD pipelines with GitHub Actions, Azure DevOps, Databricks Asset Bundles, and Terraform.
• Deploy and maintain model-serving infrastructure for batch, streaming, real-time, and API-based inference.
• Establish automated gates for model performance, data quality, fairness, safety, explainability, robustness, privacy, and release readiness.
• Implement model and feature monitoring, drift and bias detection, logging, tracing, alerting, and dashboards.
• Develop automated retraining, champion/challenger promotion, rollback, and retirement workflows.
• Implement Trustworthy AI controls, documentation, impact assessments, audit logging, safeguards, and human oversight.
• Integrate ML workflows with source control, infrastructure provisioning, secrets management, identity controls, and release processes.
• Optimize compute and serving utilization, cluster and endpoint configurations, and workload costs.
• Troubleshoot pipeline failures, deployment issues, serving incidents, and performance degradation.
• Maintain deployment templates, reusable repositories, runbooks, model documentation, release evidence, and technical standards.

Skills

Degrees

Languages

Travel

Industry

Relocation