3 days ago
Remote, United States +2 moreSenior
Base Salary
$99k - $168k/yr
Responsibilities
- Design, build, and maintain production MLOps pipelines covering model training, evaluation, validation, registration, deployment, monitoring, retraining, rollback, and retirement.
- Develop reusable MLOps and LLMOps patterns using Azure Databricks, Databricks ML, MLflow, Unity Catalog, Azure Machine Learning, Python, and approved Azure AI services.
- Implement model versioning, registries, lineage, quality metrics, model cards, approval workflows, monitoring, drift and bias detection, logging, tracing, alerting, and operational dashboards.
- Build automated deployment workflows with GitHub Actions, Azure DevOps, Databricks Asset Bundles, and Terraform across development, test, and production environments.
- Deploy and maintain batch, streaming, real-time, and API-based model-serving infrastructure using Databricks Model Serving, Azure Machine Learning, Databricks compute, serverless capabilities, and GPU-enabled configurations.
- Implement Trustworthy AI controls, model documentation, impact assessments, audit logging, content safeguards, human oversight, and evaluation evidence aligned with government standards and the NIST AI Risk Management Framework.
- Troubleshoot failed pipelines, deployment issues, model-serving incidents, performance degradation, and monitoring alerts while supporting root-cause analysis.
- Create deployment templates, reusable repositories, operational runbooks, model documentation, release evidence, and technical standards.
- Monitor compute and serving utilization, tune cluster and endpoint configurations, apply tagging conventions, and optimize training and inference costs.
Requirements
- U.S. citizenship is required, and candidates must reside and work in the United States, be authorized to work in the U.S., and have lived in the U.S. for three 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 plus four additional years of relevant experience in lieu of a bachelor’s degree.
- Six or more years of experience designing and supporting production machine learning solutions in cloud environments.
- A master’s degree may substitute for two years of relevant experience.
- Experience with MLOps, automated deployment pipelines, model lifecycle management, Python, source control systems, CI/CD frameworks, and cloud-native architecture.
- Hands-on experience with Azure Machine Learning, Databricks, MLflow, or comparable ML platforms.
- Experience with model monitoring, automated retraining, performance tracking, responsible AI, AI governance, or Trustworthy AI initiatives.
- Familiarity with infrastructure-as-code, automated environment provisioning, scalable model serving, containerization, and cloud cost optimization for AI/ML workloads.
Benefits
- Remote work arrangement with a strong preference for candidates in the Washington, DC Metro Area and occasional onsite meetings in Washington, DC.
- The role is contingent upon a contract award and is being staffed through advance interviews and offers.
- Work must be performed in the United States; foreign-location access is blocked and personal VPN connections are prohibited.
Categories
About ICF
ICF is a global consulting and technology services firm serving government and commercial clients across domains such as energy, health, climate, and digital modernization. It delivers advisory, program implementation, analytics, and software-enabled solutions on platforms like ServiceNow. Founded in 1969 and headquartered in Reston, Virginia, ICF is a public company traded on NASDAQ and works extensively with U.S. federal, state and local agencies as well as regulated industries.