Senior MLOps Engineer – Cloud AI Platform (Remote)

ICF; United States; Remote; USD 99,000 - 168,000; Full time

Job summary

ICF is seeking an experienced ML Ops Engineer to design and operate production ML pipelines in cloud environments, collaborating with data scientists, data engineers, cloud architects, and governance teams.

The role emphasizes model deployment, monitoring, automated retraining, and cost-aware optimization in a remote-first setting with occasional on-site meetings in Washington, DC area. U.S. citizenship and location requirements apply.

Qualifications

• U.S. Citizenship is required due to federal contract requirements.
• Must reside in the U.S., be authorized to work in the U.S., and all work must be performed in the U.S.
• Candidate 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 in lieu of a bachelor's degree.
• 6+ years of experience designing and supporting production machine learning solutions in cloud environments.
• Master's degree may substitute for two (2) years of relevant experience.

Responsibilities

• Design, build, and maintain production‑grade MLOps pipelines for 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 experiment tracking, model versioning, model registries, lineage, quality metrics, model cards, and approval workflows using MLflow and Unity Catalog to ensure models are discoverable, reusable, and auditable.
• Build automated CI/CD pipelines using GitHub Actions, Azure DevOps, Databricks Asset Bundles, and Terraform to promote models, code, configurations, and supporting infrastructure consistently across development, test, and production environments.
• Deploy and maintain scalable model‑serving infrastructure for batch, streaming, real‑time, and API‑based inference workloads using Databricks Model Serving, Azure Machine Learning, Databricks compute, serverless capabilities, and GPU‑enabled configurations, as applicable.
• Establish automated testing and evaluation gates for model performance, data quality, fairness, safety, explainability, robustness, privacy, and release readiness.

Skills

ML in cloud

Python

CI/CD

Education

Bachelor's degree in CS/Software/ML/Data/Engineering

Master's degree may substitute for 2 years experience

Tools

Azure Machine Learning

Databricks

MLflow

Job description

ICF is seeking an experienced ML Ops Engineer to design and operate production ML pipelines in cloud environments, collaborating with data scientists, data engineers, cloud architects, and governance teams.

The role emphasizes model deployment, monitoring, automated retraining, and cost-aware optimization in a remote-first setting with occasional on-site meetings in Washington, DC area. U.S. citizenship and location requirements apply.