ML Ops Engineer

Location: Remote within the United States, with a strong preference for candidates in the Washington, DC metro area and occasional onsite meetings in Washington, DC. All work must be performed in the U.S.; candidates must reside in the U.S. and be authorized to work there.

Salary: $98,614–$167,644 per year.

Company

hirify.global is an advisory and technology services provider delivering cloud, cybersecurity, enterprise architecture, data modernization, and digital transformation solutions for federal clients.

What you will do

• Design, build, and maintain production-grade MLOps pipelines covering model training, validation, registration, deployment, monitoring, retraining, rollback, and retirement.
• Develop reusable MLOps and LLMOps patterns with Azure Databricks, Databricks ML, MLflow, Unity Catalog, Azure Machine Learning, Python, and Azure AI services.
• Build CI/CD and infrastructure automation with GitHub Actions, Azure DevOps, Databricks Asset Bundles, and Terraform.
• Deploy scalable batch, streaming, real-time, and API-based model-serving infrastructure, including GPU-enabled configurations.
• Implement monitoring, drift and bias detection, testing gates, alerting, operational dashboards, and automated retraining workflows.
• Collaborate with data scientists, data engineers, platform engineers, DevSecOps, security, and governance stakeholders to operationalize reliable and auditable AI solutions.

Requirements

• U.S. citizenship is required.
• Must reside in the U.S., be authorized to work in the U.S., and perform all work in the U.S.
• Must have lived in the U.S. for three full years out of the last five years.
• Bachelor’s degree in computer science, software engineering, machine learning, data science, engineering, or a related field, or a high school diploma with four additional years of relevant experience.
• 6+ years of experience designing and supporting production machine learning solutions in cloud environments.
• Experience with MLOps, automated deployment pipelines, model lifecycle management, Python, CI/CD, Azure Machine Learning, Databricks, MLflow, cloud-native architecture, and infrastructure as code.

Nice to have

• Experience with Responsible AI, AI governance, or Trustworthy AI initiatives.
• Knowledge of containerization, scalable model serving, automated environment provisioning, and AI/ML cost optimization.

Culture & Benefits

• Work on a cloud-based enterprise data and analytics platform supporting a large federal agency.
• Collaborate across data science, engineering, security, DevSecOps, and governance functions.
• Full-time employment with a nationwide remote work arrangement within the United States.
• Role is contingent upon a contract award; interviews and offers are being conducted in anticipation of the award.