Remote MLOps Engineer for AI/ML Platform & Pipelines

ICF; Charlotte (NC); Remote; USD 99,000 - 168,000; Full time

Job summary

ICF is seeking a senior ML engineer in its Digital Modernization Division to design, build, and maintain production-grade MLOps pipelines on Azure Databricks and MLflow for federal workloads, with model versioning, governance, and automated CI/CD across dev, test, and production.

You will collaborate with data scientists, platform engineers, DevSecOps and governance teams to ensure trustworthy AI, cost-aware training and scalable model serving for batch, real-time, and API workloads.

Qualifications

• U.S. Citizenship is required due to federal contract requirements.
• Candidate 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 (3) full years out of the last five (5) years.
• 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 across environments.
• Deploy and maintain scalable model-serving infrastructure for batch, streaming, real-time, and API-based inference workloads using Databricks Model Serving, Azure ML, and GPU-enabled configurations.
• Establish automated testing and evaluation 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 using MLflow Model Monitoring and broader platform monitoring.
• Develop automated retraining, champion/challenger promotion, rollback, and model-retirement workflows to prevent performance degradation.

Skills

MLOps

Python

CI/CD

GitHub Actions

Azure DevOps

Terraform

Databricks

MLflow

Education

Bachelor’s degree in Computer Science / Software Engineering / ML / Data Science or related field

Master’s degree may substitute for 2 years of experience

Tools

Azure Databricks

MLflow

Unity Catalog

Azure Machine Learning

Job description

ICF is seeking a senior ML engineer in its Digital Modernization Division to design, build, and maintain production-grade MLOps pipelines on Azure Databricks and MLflow for federal workloads, with model versioning, governance, and automated CI/CD across dev, test, and production.

You will collaborate with data scientists, platform engineers, DevSecOps and governance teams to ensure trustworthy AI, cost-aware training and scalable model serving for batch, real-time, and API workloads.